All Classes Interface Summary Class Summary Enum Summary Exception Summary
| Class |
Description |
| AbruptChangeGenerator |
|
| AbstractAMRules |
|
| AbstractAMRulesFunctionBasicMlLearner |
|
| AbstractAnomalyDetector |
|
| AbstractC |
|
| AbstractCBase |
|
| AbstractChangeDetector |
Abstract Change Detector.
|
| AbstractClassifier |
|
| AbstractClassOption |
Abstract class option.
|
| AbstractClassOption |
Abstract class option.
|
| AbstractClusterer |
|
| AbstractConceptDriftGenerator |
|
| AbstractErrorWeightedVote |
AbstractErrorWeightedVote class for weighted votes based on estimates of errors.
|
| AbstractErrorWeightedVoteMultiLabel |
AbstractErrorWeightedVote class for weighted votes based on estimates of errors.
|
| AbstractFeatureRanking |
|
| AbstractGraphAxes |
AbstractGraphAxes is an abstract class offering functionality to draw axes.
|
| AbstractGraphCanvas |
AbstractGraphCanvas is an abstract class offering scaling functionality and
the structure of the underlying Axes and Plot classes.
|
| AbstractGraphPlot |
AbstractGraphPlot is an abstract class defining the structure of a Plot
class.
|
| AbstractMacroClusterer |
|
| AbstractMOAObject |
Abstract MOA Object.
|
| AbstractMultiLabelErrorMeasurer |
|
| AbstractMultiLabelLearner |
|
| AbstractMultiLabelStreamFilter |
Abstract Stream Filter.
|
| AbstractMultiTargetErrorMeasurer |
|
| AbstractOption |
Abstract option.
|
| AbstractOptionHandler |
Abstract Option Handler.
|
| AbstractRecommenderData |
|
| AbstractStreamFilter |
Abstract Stream Filter.
|
| AbstractTabPanel |
Abstract Tab Panel.
|
| AbstractTask |
Abstract Task.
|
| Accuracy |
|
| AccuracyUpdatedEnsemble |
The revised version of the Accuracy Updated Ensemble as proposed by
Brzezinski and Stefanowski in "Reacting to Different Types of Concept Drift:
The Accuracy Updated Ensemble Algorithm", IEEE Trans.
|
| AccuracyWeightedEnsemble |
The Accuracy Weighted Ensemble classifier as proposed by Wang et al.
|
| ADACC |
Anticipative and Dynamic Adaptation to Concept Changes.
|
| AdaGrad |
Implements the AdaGrad oneline optimiser for learning various linear models (binary class SVM, binary class logistic regression and linear regression).
|
| AdaHoeffdingOptionTree |
Adaptive decision option tree for streaming data with adaptive Naive
Bayes classification at leaves.
|
| AdaHoeffdingOptionTree.AdaLearningNode |
|
| AdaptiveMultiTargetRegressor |
Adaptive MultiTarget Regressor uses two learner
The first is used in first stage when high error are produced(e.g.
|
| AdaptiveNodePredictor |
|
| AdaptiveRandomForest |
Adaptive Random Forest
|
| AdaptiveRandomForestRegressor |
Implementation of AdaptiveRandomForestRegressor, an extension of AdaptiveRandomForest for classification.
|
| AddNoiseFilter |
Filter for adding random noise to examples in a stream.
|
| ADOB |
Adaptable Diversity-based Online Boosting (ADOB) is a modified version
of the online boosting, as proposed by Oza and Russell, which is aimed
at speeding up the experts recovery after concept drifts.
|
| ADWIN |
ADaptive sliding WINdow method.
|
| ADWINChangeDetector |
Drift detection method based in ADWIN.
|
| AdwinClassificationPerformanceEvaluator |
Classification evaluator that updates evaluation results using an adaptive sliding
window.
|
| AgrawalGenerator |
Stream generator for Agrawal dataset.
|
| AgrawalGenerator.ClassFunction |
|
| ALClassificationPerformanceEvaluator |
Active Learning Evaluator Interface to make AL Evaluators selectable in AL tasks.
|
| ALClassifier |
Active Learning Classifier Interface to make AL Classifiers selectable in AL tasks.
|
| Algorithm |
|
| Algorithm |
This class calculates the different measures for each algorithm
|
| ALMainTask |
This class provides a superclass for Active Learning tasks, which
enables convenient searching for those tasks for example when showing
a list of available Active Learning tasks.
|
| ALMeasureCollection |
Collection of measures used to evaluate AL tasks.
|
| ALMultiParamTask |
This task individually evaluates an active learning classifier for each
element of a set of parameter values.
|
| ALPartitionEvaluationTask |
This task extensively evaluates an active learning classifier on a stream.
|
| ALPrequentialEvaluationTask |
This task performs prequential evaluation for an active learning classifier
(testing, then training with each example in sequence).
|
| ALPreviewPanel |
ALPreviewPanel provides a graphical interface to display the latest preview
of a task thread.
|
| ALRandom |
|
| ALTabPanel |
This panel allows the user to select and configure a task, and run it.
|
| ALTaskManagerPanel |
This panel displays the running tasks for active learning experiments.
|
| ALTaskTextViewerPanel |
This panel displays text.
|
| ALTaskThread |
Task Thread for ALMainTask which supports pausing/resuming and cancelling of child threads
|
| ALUncertainty |
Active learning setting for evolving data streams.
|
| ALWindowClassificationPerformanceEvaluator |
Active Learning Wrapper for BasicClassificationPerformanceEvaluator.
|
| AMRulesClassifierFunction |
|
| AMRulesFunction |
|
| AMRulesLearner |
|
| AMRulesMultiLabelClassifier |
Method for online multi-Label classification.
|
| AMRulesMultiLabelLearner |
Adaptive Model Rules for MultiLabel problems (AMRulesML), the streaming rule learning algorithm.
|
| AMRulesMultiLabelLearnerSemiSuper |
Semi-supervised method for online multi-target regression.
|
| AMRulesMultiTargetRegressor |
AMRules Algorithm for multitarget
splitCriterionOption- Split criterion used to assess the merit of a split
weightedVoteOption - Weighted vote type
learnerOption - Learner selection
errorMeasurerOption - Measure of error for deciding which learner should predict
changeDetector - Change selection
João Duarte, João Gama, Albert Bifet, Adaptive Model Rules From High-Speed Data Streams.
|
| AMRulesMultiTargetRegressorSemiSuper |
|
| AMRulesRegressor |
|
| AMRulesRegressorFunction |
|
| AMRulesRegressorOld |
|
| AMRulesSplitCriterion |
|
| AnalyzeTab |
In this class are compared online learning algorithms on multiple datasets by
performing appropriate statistical tests.
|
| AnomalinessRatioScore |
Score for anomaly detection
percentageAnomalousAttributesOption - Percentage of anomalous attributes.
|
| AnomalyDetector |
Anomaly Detector interface to implement methods that detects change.
|
| AnyOut |
|
| AnyOutCore |
|
| ApproxSTORM |
|
| ArffFileStream |
Stream reader of ARFF files.
|
| ARFFIMTDD |
Implementation of ARFFIMTDD, an extension of FIMTDD to be used by AdaptiveRandomForestRegressor.
|
| ARFFIMTDD.InnerNode |
|
| ARFFIMTDD.LeafNode |
|
| ARFFIMTDD.Node |
|
| ARFFIMTDD.SplitNode |
|
| ArffLoader |
The Class ArffLoader.
|
| ARFHoeffdingTree |
Adaptive Random Forest Hoeffding Tree.
|
| ARFHoeffdingTree.LearningNodeNB |
|
| ARFHoeffdingTree.LearningNodeNBAdaptive |
|
| ARFHoeffdingTree.RandomLearningNode |
|
| ASHoeffdingTree |
Adaptive Size Hoeffding Tree used in Bagging using trees of different size.
|
| AssetNegotiationGenerator |
|
| AssetNegotiationGenerator.ClassFunction |
|
| Attribute |
The Class Attribute.
|
| AttributeClassObserver |
Interface for observing the class data distribution for an attribute.
|
| AttributeExpansionSuggestion |
Class for computing attribute split suggestions given a split test.
|
| AttributeSelectionPanel |
A sub panel in visualizeFeatures tab.
|
| AttributesInformation |
Class for storing the information of the attributes.
|
| AttributeSplitSuggestion |
Class for computing attribute split suggestions given a split test.
|
| AttributeStatisticsObserver |
Interface for observing the statistics for an attribute.
|
| AttributeSummaryPanel |
This panel displays summary statistics about an attribute: name, type
number/% of missing/unique values, number of distinct values.
|
| AttributeVisualizationPanel |
Creates a panel that shows a visualization of an attribute in a dataset.
|
| AutoClassDiscovery |
Class for discovering classes via reflection in the java class path.
|
| Autoencoder |
Implements an autoencoder: a neural network that attempts to reconstruct the input.
|
| AutoExpandVector<T> |
Vector with the capability of automatic expansion.
|
| AuxiliarMainTask |
Abstract Auxiliar Main Task.
|
| AuxiliarTabPanel |
This panel allows the user to select and configure a task, and run it.
|
| AuxiliarTaskManagerPanel |
This panel displays the running tasks.
|
| AWTInteractiveRenderer |
|
| AWTRenderable |
Interface representing a component that is renderable
|
| AWTRenderer |
Interface representing a component to edit an option.
|
| BaselinePredictor |
A naive algorithm which combines the global mean of all the existing
ratings, the mean rating of the user and the mean rating of the item
to make a prediction.
|
| BaselinePredictor |
|
| BasicAUCImbalancedPerformanceEvaluator |
Performance measures designed for class imbalance problems.
|
| BasicClassificationPerformanceEvaluator |
Classification evaluator that performs basic incremental evaluation.
|
| BasicClassificationPerformanceEvaluator.Estimator |
|
| BasicConceptDriftPerformanceEvaluator |
|
| BasicFeatureRanking |
Basic Feature Ranking method
João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.
|
| BasicMultiLabelClassifier |
|
| BasicMultiLabelLearner |
Binary relevance Multilabel Classifier
|
| BasicMultiLabelPerformanceEvaluator |
Multilabel Window Classification Performance Evaluator.
|
| BasicMultiTargetPerformanceEvaluator |
Regression evaluator that performs basic incremental evaluation.
|
| BasicMultiTargetPerformanceRelativeMeasuresEvaluator |
Regression evaluator that performs basic incremental evaluation.
|
| BasicMultiTargetRegressor |
Binary relevance Multi-Target Regressor
|
| BasicRegressionPerformanceEvaluator |
Regression evaluator that performs basic incremental evaluation.
|
| BatchCmd |
|
| BICO |
A instance of this class provides the BICO clustering algorithm.
|
| BinaryClassifierFromRegressor |
Function that convertes a regressor into a binary classifier
baseLearnerOption- regressor learner selection
|
| BinaryTreeNumericAttributeClassObserver |
Class for observing the class data distribution for a numeric attribute using a binary tree.
|
| BinaryTreeNumericAttributeClassObserverRegression |
Class for observing the class data distribution for a numeric attribute using a binary tree.
|
| BOLE |
|
| BooleanParameter |
|
| BootstrappedStream |
Bootstrapped Stream
|
| BRISMFPredictor |
Implementation of the algorithm described in Scalable
Collaborative Filtering Approaches for Large Recommender
Systems (Gábor Takács, István Pilászy, Bottyán Németh,
and Domonkos Tikk).
|
| BRISMFPredictor |
Implementation of the algorithm described in Scalable
Collaborative Filtering Approaches for Large Recommender
Systems (Gábor Takács, István Pilászy, Bottyán Németh,
and Domonkos Tikk).
|
| BucketManager |
|
| Budget |
This is an interface for classes that are to be given along with every data
point inserted in the tree.
|
| BudgetManager |
Budget Manager Interface to make AL Classifiers select the most beneficial
instances.
|
| Buffer |
This class is the buffer where the threads get each task to execute
|
| CachedInstancesStream |
Stream generator for representing a stream that is cached in memory.
|
| CacheShuffledStream |
Task for storing and shuffling examples in memory.
|
| CAND |
Continuously Adaptive Neural networks for Data streams
|
| CantellisInequality |
Returns the probability for anomaly detection according to a Cantelli inequality
mean- mean of a data variable
sd- standard deviation of a data variable
value- current value of the variable
|
| Capabilities |
Container class representing the set of capabilities an object
has.
|
| CapabilitiesHandler |
Interface marking classes as being able to specify the capabilities
they can handle.
|
| Capability |
Class enumerating the different possible capabilities of objects in
MOA.
|
| CapabilityRequirement |
Represents a requirement that a set of capabilities must meet.
|
| CategoricalParameter |
|
| CDF_Normal |
This class contains routines to calculate the
normal cumulative distribution function (CDF) and
its inverse.
|
| CDTaskManagerPanel |
This panel displays the running tasks.
|
| CFCluster |
|
| ChangeDetectedMessage |
|
| ChangeDetectionMeasures |
|
| ChangeDetector |
Change Detector interface to implement methods that detects change.
|
| ChangeDetectorLearner |
Class for detecting concept drift and to be used as a learner.
|
| CharacteristicVector |
The Characteristic Vector of a density grid is defined in
Definition 3.2 of Chen and Tu 2007 as:
The characteristic vector of a grid g is a tuple (tg,tm,D, label,status),
where tg is the last time when g is updated, tm is the last time when g
is removed from grid list as a sporadic grid (if ever), D is the grid
density at the last update, label is the class label of the grid, and
status = {SPORADIC, NORMAL} is a label used for removing sporadic grids.
|
| ChebyshevInequality |
Returns the probability for anomaly detection according to a Chebyshev inequality
mean- mean of a data variable
sd- standard deviation of a data variable
value- current value of the variable
|
| ClassificationMainTask |
Abstract Classification Main Task.
|
| ClassificationMeasureCollection |
Classification Measure Collection interface that it is used to not appear in clustering
|
| ClassificationPerformanceEvaluator |
|
| ClassificationTabPanel |
This panel allows the user to select and configure a task, and run it.
|
| Classifier |
Classifier interface for incremental classification models.
|
| ClassifierWithFeatureImportance |
Classifier with Feature Importance
|
| ClassOption |
Class option.
|
| ClassOption |
Class option.
|
| ClassOptionEditComponent |
An OptionEditComponent that lets the user edit a class option.
|
| ClassOptionSelectionPanel |
Creates a panel that displays the classes available, letting the user select
a class.
|
| ClassOptionWithListenerOption |
ClassOption that can be given a ChangeListener.
|
| ClassOptionWithListenerOptionEditComponent |
|
| ClassOptionWithNames |
|
| ClassOptionWithNamesEditComponent |
|
| ClassOptionWithNamesSelectionPanel |
|
| ClusKernel |
Representation of an Entry in the tree
|
| Cluster |
|
| Clusterer |
|
| ClusterEvent |
|
| ClusterEventListener |
|
| ClusterGenerator |
|
| Clustering |
|
| ClusteringAlgoPanel |
|
| ClusteringEvalPanel |
|
| ClusteringFeature |
Provides a ClusteringFeature.
|
| ClusteringSetupTab |
|
| ClusteringStream |
|
| ClusteringTabPanel |
|
| ClusteringTreeHeadNode |
Provides a ClusteringTreeNode with an extended nearest neighbor search in the
root.
|
| ClusteringTreeNode |
Provides a tree of ClusterFeatures.
|
| ClusteringVisualEvalPanel |
|
| ClusteringVisualTab |
|
| ClusterPanel |
|
| Clustream |
Citation: CluStream: Charu C.
|
| ClustreamKernel |
|
| ClusTree |
Citation: ClusTree: Philipp Kranen, Ira Assent, Corinna Baldauf, Thomas Seidl:
The ClusTree: indexing micro-clusters for anytime stream mining.
|
| CMM |
|
| CMM_GTAnalysis |
|
| CobWeb |
Class implementing the Cobweb and Classit clustering algorithms.
|
| CodeCellBuilder |
Implement a code cell
|
| ColorArray |
|
| ColorGenerator |
This interface specifies the generateColors method for classes
which generate colors according different strategies such that
those colors can be distinguished easily.
|
| ColorObject |
|
| ComposedSplitFunction<DATA> |
|
| ConceptDriftGenerator |
|
| ConceptDriftMainTask |
|
| ConceptDriftMainTask |
|
| ConceptDriftRealStream |
Stream generator that adds concept drift to examples in a stream with
different classes and attributes.
|
| ConceptDriftStream |
Stream generator that adds concept drift to examples in a stream.
|
| ConceptDriftTabPanel |
This panel allows the user to select and configure a task, and run it.
|
| Configurable |
Configurable interface.
|
| ConfStream |
|
| Converter |
Converter.
|
| CoresetCostTriple |
CoresetCostTriple is a wrapper that allows the lloydPlusPlus method in StreamKM to return the coresetCentres,
radii of the associated clusters and the cost associated with the coreset.
|
| CoresetKMeans |
Provides methods to execute the k-means and k-means++ algorithm with a
clustering.
|
| Cramer |
Implements the Multivariate Non-parametric Cramer Von Mises Statistical Test.
|
| CSMOTE |
CSMOTE
|
| CuckooHashing<T> |
Provides a hash table based on Cuckoo Hashing.
|
| CusumDM |
Drift detection method based in Cusum
|
| DACC |
Dynamic Adaptation to Concept Changes.
|
| DataObject |
This object encapsulates a data point.
|
| DataPoint |
|
| Dataset |
|
| DataSet |
A set of DataObjects.
|
| DBScan |
|
| DDM |
Drift detection method based in DDM method of Joao Gama SBIA 2004.
|
| DecisionStump |
Decision trees of one level.
Parameters:
|
| DenseInstance |
The Class DenseInstance.
|
| DenseInstanceData |
The Class DenseInstanceData.
|
| DenseMicroCluster |
|
| DenseVector |
|
| DensityGrid |
Density Grids are defined in equation 3 (section 3.1) of Chen and Tu 2007 as:
In D-Stream, we partition the d−dimensional space S into density grids.
|
| DependentOptionsUpdater |
This class handles the dependency between two options by updating the
dependent option whenever the option it is depending on changes.
|
| DietzfelbingerHash |
Provides a Dietzfelbinger hash function.
|
| DiscreteAttributeClassObserver |
Interface for observing the class data distribution for a discrete (nominal) attribute.
|
| DistanceFunction |
Interface for any class that can compute and return distances between two
instances.
|
| DistanceFunction<DATA> |
An object that can calculate the distance between two data objects.
|
| DistanceFunctions |
|
| DistanceFunctions.EuclideanCoordinate |
An interface to represent coordinates in Euclidean spaces.
|
| DominantLabelsClassifier |
|
| DoTask |
Class for running a MOA task from the command line.
|
| DoubleVector |
Vector of double numbers with some utilities.
|
| DriftDetectionMethodClassifier |
Class for handling concept drift datasets with a wrapper on a
classifier.
|
| Dstream |
Citation: Y.
|
| DynamicWeightedMajority |
Dynamic weighted majority algorithm.
|
| EDDM |
Drift detection method based in EDDM method of Manuel Baena et al.
|
| EditableMultiChoiceOption |
MultiChoiceOption that can have changing options.
|
| EditableMultiChoiceOptionEditComponent |
|
| EFDT |
|
| EFDT.ActiveLearningNode |
|
| EFDT.EFDTNode |
|
| EFDT.FoundNode |
|
| EFDT.InactiveLearningNode |
|
| EFDT.LearningNode |
|
| EFDT.LearningNodeNB |
|
| EFDT.LearningNodeNBAdaptive |
|
| EFDT.Node |
|
| EFDT.SplitNode |
|
| EMProjectedClustering |
Implements clustering via Expectation Maximization
but return a clear partitioning of the data,
i.e.
|
| EMTopDownTreeBuilder |
|
| EnsembleClustererAbstract |
|
| EnsembleDriftDetectionMethods |
Ensemble Drift detection method
|
| EntropyCollection |
|
| EntropyThreshold |
Entropy measure use by online multi-label AMRules for heuristics computation.
|
| Entry |
|
| ErrorMeasurement |
Computes error measures with a fading factor
fadingErrorFactorOption - Fading factor
|
| ErrorWeightedVote |
ErrorWeightedVote interface for weighted votes based on estimates of errors.
|
| ErrorWeightedVoteMultiLabel |
ErrorWeightedVoteMultiLabel interface for weighted votes based on estimates of errors.
|
| EuclideanDistance |
Implementing Euclidean distance (or similarity) function.
One object defines not one distance but the data model in which the distances between objects of that data model can be computed.
Attention: For efficiency reasons the use of consistency checks (like are the data models of the two instances exactly the same), is low.
For more information, see:
Wikipedia.
|
| EvaluateClustering |
Task for evaluating a clusterer on a stream.
|
| EvaluateConceptDrift |
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
| EvaluateConceptDrift |
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
| EvaluateInterleavedChunks |
|
| EvaluateInterleavedChunks |
|
| EvaluateInterleavedTestThenTrain |
Task for evaluating a classifier on a stream by testing then training with
each example in sequence.
|
| EvaluateInterleavedTestThenTrain |
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
| EvaluateModel |
Task for evaluating a static model on a stream.
|
| EvaluateModelMultiLabel |
Task for evaluating a static model on a stream.
|
| EvaluateModelMultiTarget |
Task for evaluating a static model on a stream.
|
| EvaluateModelRegression |
Task for evaluating a static model on a stream.
|
| EvaluateMultipleClusterings |
Task for evaluating a clusterer on multiple (related) streams.
|
| EvaluateOnlineRecommender |
Test for evaluating a recommender by training and periodically testing
on samples from a rating dataset.
|
| EvaluatePeriodicHeldOutTest |
Task for evaluating a classifier on a stream by periodically testing on a
heldout set.
|
| EvaluatePeriodicHeldOutTest |
Task for evaluating a classifier on a stream by periodically testing on a heldout set.
|
| EvaluatePrequential |
|
| EvaluatePrequential |
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
| EvaluatePrequentialCV |
Task for prequential cross-validation evaluation of a classifier on a stream by testing then training with each
example in sequence and doing cross-validation at the same time.
|
| EvaluatePrequentialCV |
Task for prequential cross-validation evaluation of a classifier on a stream by testing then training with each
example in sequence and doing cross-validation at the same time.
|
| EvaluatePrequentialDelayed |
Task for evaluating a classifier on a delayed stream by testing and only
training with the example after k other examples (delayed labeling).
|
| EvaluatePrequentialDelayedCV |
Task for delayed cross-validation evaluation of a classifier on a
stream by testing and only training with the example after the arrival of
other k examples (delayed labeling).
|
| EvaluatePrequentialMultiLabel |
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
| EvaluatePrequentialMultiTarget |
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
| EvaluatePrequentialMultiTargetSemiSuper |
Multi-target Prequential semi-supervised evaluation
Phase1: Creates a initial model with of the instances in the dataset
Phase2: When an instance is received:
A binary random process with a binomial distribution selects if the instance should be
labeled or unlabeled with probability .
|
| EvaluatePrequentialRegression |
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
| EWMAChartDM |
Drift detection method based in EWMA Charts of Ross, Adams, Tasoulis and Hand
2012
|
| EWMAClassificationPerformanceEvaluator |
Classification evaluator that updates evaluation results using an Exponential Weighted Moving Average.
|
| ExactSTORM |
|
| Example<T> |
|
| ExampleStream<E extends Example> |
Interface representing a data stream of examples.
|
| ExperimenterTabPanel |
|
| ExperimenterTask |
|
| ExperimeterCLI |
|
| ExpNegErrorWeightedVote |
ExpNegErrorWeightedVote class for weighted votes based on estimates of errors.
|
| ExpPreviewPanel |
This panel displays the running task preview text and buttons.
|
| ExpPreviewPanel.TypePanel |
|
| ExpTaskThread |
Task Thread.
|
| ExpTaskThread.Status |
|
| F1 |
|
| FadingFactorClassificationPerformanceEvaluator |
Classification evaluator that updates evaluation results using a fading factor.
|
| FadingTargetMean |
|
| FailedTaskReport |
Class for reporting a failed task.
|
| FastVector<E> |
Simple extension of ArrayList.
|
| FeatureAnalysisTabPanel |
FeatureAnalysis module panel.
|
| FeatureImportanceClassifier |
Feature Importance Classifier
|
| FeatureImportanceConfig |
This class Provides GUI to user so that they can configure parameters for feature importance algorithm.
|
| FeatureImportanceDataModelPanel |
This is a sub panel in FeatureImportance tab.
|
| FeatureImportanceGraph |
This is a sub panel in FeatureImportance tab.
|
| FeatureImportanceHoeffdingTree |
HoeffdingTree Feature Importance extends the traditional HoeffdingTree classifier to also yield feature importances.
|
| FeatureImportanceHoeffdingTreeEnsemble |
HoeffdingTree Ensemble Feature Importance.
|
| FeatureImportancePanel |
This panel is the FeatureImportance tab which provides config GUI for feature importance algorithm,
run button to trigger the execution of the algorithm, table line graphs to display scores of the
the execution result.
|
| FeatureRanking |
|
| FeatureRankingMessage |
|
| Fichero |
|
| FileExtensionFilter |
A filter that is used to restrict the files that are shown.
|
| FileOption |
File option.
|
| FileOptionEditComponent |
An OptionEditComponent that lets the user edit a file option.
|
| FileStream |
|
| FilteredSparseInstance |
The Class FilteredSparseInstance.
|
| FilteredSparseInstanceData |
The Class FilteredSparseInstanceData.
|
| FilteredStream |
Class for representing a stream that is filtered.
|
| FIMTDD |
Implementation of FIMTDD, regression and model trees for data streams.
|
| FIMTDD.InnerNode |
|
| FIMTDD.LeafNode |
|
| FIMTDD.Node |
|
| FIMTDD.SplitNode |
|
| FIMTDDNumericAttributeClassLimitObserver |
|
| FIMTDDNumericAttributeClassObserver |
|
| FirstHitVoteMultiLabel |
FirstHitVoteMultiLabel class for weighted votes based on estimates of errors.
|
| FixedBM |
|
| FixedLengthList<E> |
FixedLengthList is an extension of an ArrayList with a fixed maximum size.
|
| FlagOption |
Flag option.
|
| FlagOptionEditComponent |
An OptionEditComponent that lets the user edit a flag option.
|
| FlixsterDataset |
|
| FloatOption |
Float option.
|
| FloatOptionEditComponent |
An OptionEditComponent that lets the user edit a float option.
|
| GaussianEstimator |
Gaussian incremental estimator that uses incremental method that is more resistant to floating point imprecision.
|
| GaussianNumericAttributeClassObserver |
Class for observing the class data distribution for a numeric attribute using gaussian estimators.
|
| GaussInequality |
Returns the probability for anomaly detection according to a Gauss inequality
mean- mean of a data variable
sd- standard deviation of a data variable
value- current value of the variable
|
| General |
|
| GeometricMovingAverageDM |
Drift detection method based in Geometric Moving Average Test
|
| GiniSplitCriterion |
Class for computing splitting criteria using Gini
with respect to distributions of class values.
|
| Globals |
Class for storing global information about current version of MOA.
|
| GradualChangeGenerator |
|
| GraphAxes |
|
| GraphCanvas |
|
| GraphCurve |
|
| GraphMultiCurve |
GraphMultiCurve is an an implementation of AbstractGraphPlot that draws
several curves on a Canvas.
|
| GraphScatter |
GraphScatter is an implementation of AbstractGraphPlot that draws a scatter
plot.
|
| GreenwaldKhannaNumericAttributeClassObserver |
Class for observing the class data distribution for a numeric attribute using Greenwald and Khanna methodology.
|
| GreenwaldKhannaQuantileSummary |
Class for representing summaries of Greenwald and Khanna quantiles.
|
| GreenwaldKhannaQuantileSummary.Tuple |
|
| GridCluster |
Grid Clusters are defined in Definition 3.6 of Chen and Tu 2007 as:
Let G =(g1, ·· · ,gm) be a grid group, if every inside grid of G is
a dense grid and every outside grid is either a dense grid or a
transitional grid, then G is a grid cluster.
|
| GUI |
The main class for the MOA gui.
|
| GUIDefaults |
This class offers get methods for the default GUI settings in
the props file moa/gui/GUI.props.
|
| GUIUtils |
This class offers util methods for displaying dialogs showing errors or exceptions.
|
| Hash |
|
| HashingTrickFilter |
Filter to perform feature hashing to reduce the number of attributes by applying
a hash function to features.
|
| HDDM_A_Test |
Online drift detection method based on Hoeffding's bounds.
|
| HDDM_W_Test |
Online drift detection method based on McDiarmid's bounds.
|
| HDDM_W_Test.SampleInfo |
|
| HeterogeneousEnsembleAbstract |
BLAST (Best Last) for Heterogeneous Ensembles Abstract Base Class
|
| HeterogeneousEnsembleBlast |
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factors
|
| HeterogeneousEnsembleBlastFadingFactors |
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factors
|
| HoeffdingAdaptiveTree |
Hoeffding Adaptive Tree for evolving data streams.
|
| HoeffdingAdaptiveTree.AdaLearningNode |
|
| HoeffdingAdaptiveTree.AdaSplitNode |
|
| HoeffdingAdaptiveTree.NewNode |
|
| HoeffdingAdaptiveTreeClassifLeaves |
Hoeffding Adaptive Tree for evolving data streams that has a classifier at
the leaves.
|
| HoeffdingOptionTree |
Hoeffding Option Tree.
|
| HoeffdingOptionTree.ActiveLearningNode |
|
| HoeffdingOptionTree.FoundNode |
|
| HoeffdingOptionTree.InactiveLearningNode |
|
| HoeffdingOptionTree.LearningNode |
|
| HoeffdingOptionTree.LearningNodeNB |
|
| HoeffdingOptionTree.LearningNodeNBAdaptive |
|
| HoeffdingOptionTree.Node |
|
| HoeffdingOptionTree.SplitNode |
|
| HoeffdingTree |
Hoeffding Tree or VFDT.
|
| HoeffdingTree.ActiveLearningNode |
|
| HoeffdingTree.FoundNode |
|
| HoeffdingTree.InactiveLearningNode |
|
| HoeffdingTree.LearningNode |
|
| HoeffdingTree.LearningNodeNB |
|
| HoeffdingTree.LearningNodeNBAdaptive |
|
| HoeffdingTree.Node |
|
| HoeffdingTree.SplitNode |
|
| HoeffdingTreeClassifLeaves |
Hoeffding Tree that have a classifier at the leaves.
|
| HSTreeNode |
A node in an HSTree.
|
| HSTrees |
Implements the Streaming Half-Space Trees one-class classifier described in
S.
|
| HSVColorGenerator |
This class generates colors in the HSV space.
|
| HyperplaneGenerator |
Stream generator for Hyperplane data stream.
|
| Iadem2 |
|
| Iadem3 |
|
| Iadem3.restartsVariablesAtDrift |
|
| Iadem3Subtree |
|
| IademAttributeSplitSuggestion |
|
| IademCommonProcedures |
|
| IademException |
|
| IademGaussianNumericAttributeClassObserver |
|
| IademGreenwaldKhannaNumericAttributeClassObserver |
|
| IademGreenwaldKhannaQuantileSummary |
|
| IademNominalAttributeBinaryTest |
|
| IademNominalAttributeMultiwayTest |
|
| IademNumericAttributeBinaryTest |
|
| IademNumericAttributeObserver |
|
| IademSplitCriterion |
|
| IademVFMLNumericAttributeClassObserver |
|
| ICVarianceReduction |
|
| IDenseMacroCluster |
|
| IMacroClusterer |
|
| ImageChart |
This class allows to handle the properties of the graph created by
JFreeChart.
|
| ImagePanel |
This class creates a panel with an image.
|
| ImageTreePanel |
This class creates a JTree panel to show the images generated with
JFreeChart.
|
| ImageViewer |
This class creates a window where images generated with JFreeChart are
displayed.
|
| ImbalancedStream |
Imbalanced Stream.
|
| ImmutableCapabilities |
Set of capabilities that cannot be modified after creation.
|
| InfoGainSplitCriterion |
Class for computing splitting criteria using information gain
with respect to distributions of class values.
|
| InfoGainSplitCriterionMultilabel |
Class for computing splitting criteria using information gain with respect to
distributions of class values for Multilabel data.
|
| InfoPanel |
|
| InputAttributesSelector |
|
| InputStreamProgressMonitor |
Class for monitoring the progress of reading an input stream.
|
| Instance |
The Interface Instance.
|
| InstanceAttributesSelector |
Transforms instances considering both a subset of input attributes
and a subset of output attributes
|
| InstanceConditionalBinaryTest |
Abstract binary conditional test for instances to use to split nodes in Hoeffding trees.
|
| InstanceConditionalTest |
Abstract conditional test for instances to use to split nodes in Hoeffding trees.
|
| InstanceData |
The Interface InstanceData.
|
| InstanceExample |
|
| InstanceImpl |
The Class InstanceImpl.
|
| InstanceInformation |
The Class InstanceInformation.
|
| InstanceOutputAttributesSelector |
Transforms instances considering only a subset of output attributes
|
| Instances |
The Class Instances.
|
| InstancesHeader |
Class for storing the header or context of a data stream.
|
| InstancesSummaryPanel |
This panel just displays relation name, number of instances, and number of
attributes.
|
| InstanceStream |
Interface representing a data stream of instances.
|
| InstanceTransformer |
Interface for instance transformation
|
| IntegerParameter |
|
| IntOption |
Int option.
|
| IntOptionEditComponent |
An OptionEditComponent that lets the user edit an integer option.
|
| InverseErrorWeightedVote |
InverseErrorWeightedVoteMultiLabel class for weighted votes based on estimates of errors.
|
| InverseErrorWeightedVoteMultiLabel |
InverseErrorWeightedVoteMuliLabel class for weighted votes based on estimates of errors.
|
| IParameter |
|
| IrrelevantFeatureAppenderStream |
IrrelevantFeatureAppender Stream.
|
| ISBIndex |
|
| ISBIndex |
|
| ISBIndex |
|
| ISBIndex |
|
| ISBIndex.ISBNode |
|
| ISBIndex.ISBNode |
|
| ISBIndex.ISBNode |
|
| ISBIndex.ISBNode |
|
| ISBIndex.ISBNode.NodeType |
|
| ISBIndex.ISBSearchResult |
|
| ISBIndex.ISBSearchResult |
|
| ISBIndex.ISBSearchResult |
|
| ISBIndex.ISBSearchResult |
|
| ISOUPTree |
iSOUPTree class for structured output prediction.
|
| ISOUPTree.InnerNode |
|
| ISOUPTree.LeafNode |
|
| ISOUPTree.Node |
|
| ISOUPTree.SplitNode |
|
| ISOUPTreeRF |
|
| JavaCLIParser |
Java Command Line Interface Parser.
|
| JesterDataset |
|
| KDTree |
Class implementing the KDTree search algorithm for nearest neighbour search.
The connection to dataset is only a reference.
|
| KDTreeNode |
A class representing a KDTree node.
|
| KDTreeNodeSplitter |
Class that splits up a KDTreeNode.
|
| KMeans |
A kMeans implementation for microclusterings.
|
| KMeansInpiredMethod |
The class that splits a node into two such that the overall sum of squared distances of points to their centres on both sides of the (axis-parallel) splitting plane is minimum.
For more information see also:
Ashraf Masood Kibriya (2007).
|
| kNN |
k Nearest Neighbor.
|
| KNN |
Implements the multivariate non-parametric KNN statistical test.
|
| kNNwithPAW |
k Nearest Neighbor ADAPTIVE with PAW.
|
| kNNwithPAWandADWIN |
k Nearest Neighbor ADAPTIVE with ADWIN+PAW.
|
| Learner<E extends Example> |
Learner interface for incremental learning models.
|
| LearnerSemiSupervised<E extends Example> |
|
| LearningCurve |
Class that stores and keeps the history of evaluation measurements.
|
| LearningEvaluation |
Class that stores an array of evaluation measurements.
|
| LearningLiteral |
|
| LearningLiteralClassification |
This class contains the functions for learning the literals for Multi-label classification
(in same way as Multi-Target regression).
|
| LearningLiteralRegression |
|
| LearningPerformanceEvaluator<E extends Example> |
Interface implemented by learner evaluators to monitor
the results of the learning process.
|
| LearnModel |
Task for learning a model without any evaluation.
|
| LearnModelMultiLabel |
Task for learning a model without any evaluation.
|
| LearnModelMultiTarget |
Task for learning a model without any evaluation.
|
| LearnModelRegression |
Task for learning a model without any evaluation.
|
| LearnNSE |
Ensemble of classifiers-based approach for incremental learning of concept
drift, characterized by nonstationary environments (NSEs), where the
underlying data distributions change over time.
|
| LEDGenerator |
Stream generator for the problem of predicting the digit displayed on a 7-segment LED display.
|
| LEDGeneratorDrift |
Stream generator for the problem of predicting the digit displayed on a 7-segment LED display with drift.
|
| LeveragingBag |
Leveraging Bagging for evolving data streams using ADWIN.
|
| LimAttClassifier |
Ensemble Combining Restricted Hoeffding Trees using Stacking.
|
| LimAttHoeffdingTree |
Hoeffding decision trees with a restricted number of attributes for data
streams.
|
| LimAttHoeffdingTree.LearningNodeNB |
|
| LimAttHoeffdingTree.LearningNodeNBAdaptive |
|
| LimAttHoeffdingTree.LimAttLearningNode |
|
| LineAndScatterPanel |
This is a sub panel in VisualizeFeatures tab.
|
| LinearNNSearch |
Class implementing the brute force search algorithm for nearest neighbour search.
|
| LineGraphViewPanel |
This panel displays an evaluation learning curve.
|
| ListOption |
List option.
|
| ListOptionEditComponent |
An OptionEditComponent that lets the user edit a list option.
|
| Literal |
|
| LookAndFeel |
Manages setting the look and feel.
|
| LowPassFilteredLearner |
|
| MainTask |
Abstract Main Task.
|
| MajorityClass |
Majority class learner.
|
| MajorityLabelset |
Majority Labelset classifier.
|
| MakeObject |
Class for writing a MOA object to a file from the command line.
|
| MarkDownCellBuilder |
Implement a markdown cell
|
| MCOD |
|
| MCODBase |
|
| MCODBase.EventItem |
|
| MCODBase.EventQueue |
|
| MeanAbsoluteDeviation |
Computes the Mean Absolute Deviation for single target regression problems
|
| MeanAbsoluteDeviationMT |
Mean Absolute Deviation for multitarget and with fading factor
|
| MeanPreviewCollection |
|
| Measure |
This class determines the value of each measure for each algorithm
|
| MeasureCollection |
|
| Measurement |
Class for storing an evaluation measurement.
|
| MeasureOverview |
MeasureOverview provides a graphical overview of the current and mean
measure values during the runtime of a task.
|
| MeasureStreamSpeed |
Task for measuring the speed of the stream.
|
| MedianOfWidestDimension |
The class that splits a KDTree node based on the median value of a dimension in which the node's points have the widest spread.
For more information see also:
Jerome H.
|
| MEKAClassifier |
Wrapper for MEKA classifiers.
|
| MembershipMatrix |
|
| MemRecommenderData |
|
| MemRecommenderData |
|
| MeritCheckMessage |
|
| MeritFeatureRanking |
Merit Feature Ranking method
João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.
|
| MeritThreshold |
Input selection algorithm based on Merit threshold
|
| MetaMainTask |
This class provides features for handling tasks in a tree-like
structure of parents and subtasks.
|
| MetaMultilabelGenerator |
Stream generator for multilabel data.
|
| Metric |
Provides methods to calculate different distances of points.
|
| MicroCluster |
|
| MicroCluster |
|
| MidPointOfWidestDimension |
The class that splits a KDTree node based on the midpoint value of a dimension in which the node's points have the widest spread.
For more information see also:
Andrew Moore (1991).
|
| MinErrorWeightedVote |
MinErrorWeightedVote class for weighted votes based on estimates of errors.
|
| Miniball |
Java Porting of the Miniball.h code of Bernd Gaertner.
|
| MiscUtils |
Class implementing some utility methods.
|
| MixedGenerator |
Abrupt concept drift, boolean noise-free examples.
|
| MixedGenerator.ClassFunction |
|
| MLCviaMTR |
|
| MLP |
|
| MLP.NormalizeInfo |
|
| MOA |
Wrapper for MOA classifiers.
Since MOA doesn't offer a mechanism to query a classifier for the types of attributes and classes it can handle, the capabilities of this wrapper are hard-coded: nominal and numeric attributes and only nominal class attributes are allowed.
|
| MOA |
A wrapper around MOA instance streams.
|
| MOAClassOptionEditor |
An editor for MOA ClassOption objects.
|
| MOAObject |
Interface implemented by classes in MOA, so that all are serializable,
can produce copies of their objects, and can measure its memory size.
|
| MOAUtils |
A helper class for MOA related classes.
|
| MovielensDataset |
|
| MTRandom |
|
| MTree<DATA> |
The main class that implements the M-Tree.
|
| MultiChoiceOption |
Multi choice option.
|
| MultiChoiceOptionEditComponent |
An OptionEditComponent that lets the user edit a multi choice option.
|
| MultiClassClassifier |
Multiclass classifier interface for incremental classifier models.
|
| MultiFilteredStream |
Class for representing a stream that is filtered.
|
| MultilabelArffFileStream |
Stream reader for ARFF files of multilabel data.
|
| MultiLabelBSTree |
Binary search tree for AMRules splitting points determination
|
| MultiLabelBSTreeFloat |
|
| MultiLabelBSTreePCT |
|
| MultiLabelClassifier |
|
| MultiLabelErrorMeasurer |
|
| MultiLabelFilteredStream |
Class for representing a stream that is filtered.
|
| MultilabelHoeffdingTree |
Hoeffding Tree for classifying multi-label data.
|
| MultilabelHoeffdingTree.MultilabelInactiveLearningNode |
|
| MultilabelInformationGain |
Multi-label Information Gain.
|
| MultilabelInstance |
Multilabel instance.
|
| MultiLabelInstance |
The Interface MultiLabelInstance.
|
| MultilabelInstancesHeader |
Class for storing the header or context of a multilabel data stream.
|
| MultiLabelLearner |
|
| MultiLabelMainTask |
|
| MultiLabelNaiveBayes |
Binary relevance with Naive Bayes
|
| MultiLabelNominalAttributeObserver |
Function for determination of splitting points for nominal variables
|
| MultiLabelPerceptronClassification |
Multi-Label perceptron classifier (by Binary Relevance).
|
| MultiLabelPerformanceEvaluator |
Interface implemented by learner evaluators to monitor
the results of the regression learning process.
|
| MultiLabelPrediction |
|
| MultiLabelRandomAMRules |
|
| MultiLabelRule |
|
| MultiLabelRuleClassification |
|
| MultiLabelRuleRegression |
|
| MultiLabelRuleSet |
|
| MultiLabelSplitCriterion |
|
| MultiLabelStreamFilter |
|
| MultiLabelTabPanel |
This panel allows the user to select and configure a task, and run it.
|
| MultiLabelTaskManagerPanel |
This panel displays the running tasks.
|
| MultiLabelVote |
|
| MultiTargetArffFileStream |
Stream reader of ARFF files.
|
| MultiTargetArffLoader |
|
| MultiTargetErrorMeasurer |
|
| MultiTargetInstanceStream |
Interface representing a data stream of instances.
|
| MultiTargetLearnerSemiSupervised |
|
| MultiTargetMainTask |
|
| MultiTargetMeanRegressor |
Target mean regressor
|
| MultiTargetNoChange |
MultiTargetNoChange class regressor.
|
| MultiTargetPerceptronRegressor |
Binary relevance with a regression perceptron
|
| MultiTargetPerformanceEvaluator |
Interface implemented by learner evaluators to monitor
the results of the regression learning process.
|
| MultiTargetRegressor |
MultiTargetRegressor interface for incremental MultiTarget regression models.
|
| MultiTargetTabPanel |
This panel allows the user to select and configure a task, and run it.
|
| MultiTargetTaskManagerPanel |
This panel displays the running tasks.
|
| MultiTargetVarianceRatio |
|
| MultiTargetWindowRegressionPerformanceEvaluator |
Multi-target regression evaluator that updates evaluation results using a sliding window.
|
| MultiTargetWindowRegressionPerformanceRelativeMeasuresEvaluator |
Multi-target regression evaluator that updates evaluation results using a sliding window.
|
| MyBaseOutlierDetector |
|
| MyBaseOutlierDetector.Outlier |
|
| MyBaseOutlierDetector.OutlierNotifier |
|
| MyBaseOutlierDetector.PrintMsg |
|
| MyBaseOutlierDetector.ProgressInfo |
|
| NaiveBayes |
Naive Bayes incremental learner.
|
| NaiveBayesMultinomial |
Class for building and using a multinomial Naive
Bayes classifier.
|
| NearestNeighbourDescription |
Implements David Tax's Nearest Neighbour Description method described in
Section 3.4.2 of D.
|
| NearestNeighbourSearch |
Abstract class for nearest neighbour search.
|
| NoAnomalyDetection |
No anomaly detection is performed
|
| NoChange |
NoChange class classifier.
|
| NoChangeDetection |
|
| NoChangeGenerator |
|
| Node |
|
| NoFeatureRanking |
No feature ranking is performed
|
| NoInstanceTransformation |
Performs no transformation.
|
| NominalAttributeBinaryRulePredicate |
Nominal binary conditional test for instances to use to split nodes in rules.
|
| NominalAttributeBinaryTest |
Nominal binary conditional test for instances to use to split nodes in Hoeffding trees.
|
| NominalAttributeClassObserver |
Class for observing the class data distribution for a nominal attribute.
|
| NominalAttributeMultiwayTest |
Nominal multi way conditional test for instances to use to split nodes in Hoeffding trees.
|
| NominalRulePredicate |
Class that contains the literal information for a nominal variable
|
| NominalStatisticsObserver |
|
| NonConvexCluster |
|
| NormalisationFilter |
Filter for standardising and normalising instances in a stream.
|
| NormalizableDistance |
Represents the abstract ancestor for normalizable distance functions, like
Euclidean or Manhattan distance.
|
| NotebookBuilder |
Manage the list of all cells
Add new cells
Create a Jupyter NotebookBuilder as IPYNB file
|
| NotebookCellBuilder |
Abstract class of a cell
|
| NullAttributeClassObserver |
Class for observing the class data distribution for a null attribute.
|
| NullMonitor |
Class that represents a null monitor.
|
| NumericalParameter |
|
| NumericAttributeBinaryRulePredicate |
Numeric binary conditional test for instances to use to split nodes in
AMRules.
|
| NumericAttributeBinaryTest |
Numeric binary conditional test for instances to use to split nodes in Hoeffding trees.
|
| NumericAttributeClassObserver |
Interface for observing the class data distribution for a numeric attribute.
|
| NumericRulePredicate |
Class that contains the literal information for a numerical variable
|
| NumericStatisticsObserver |
|
| ObjectRepository |
Interface for object repositories.
|
| ObservableMOAObject |
|
| ObserverMOAObject |
|
| OCBoost |
Online Coordinate boosting for two classes evolving data streams.
|
| OddsRatioScore |
Score for anomaly detection: OddsRatio
thresholdOption - The threshold value for detecting anomalies
minNumberInstancesOption - The minimum number of instances required to perform anomaly detection
probabilityFunctionOption - Probability function selection
|
| OneClassClassifier |
An interface for incremental classifier models.
|
| OneMinusErrorWeightedVote |
|
| OnlineAccuracyUpdatedEnsemble |
The online version of the Accuracy Updated Ensemble as proposed by
Brzezinski and Stefanowski in "Combining block-based and online methods
in learning ensembles from concept drifting data streams", Information Sciences, 2014.
|
| OnlineAdaBoost |
Online AdaBoost is the online version of the boosting ensemble method AdaBoost
|
| OnlineAdaC2 |
OnlineAdaC2 is the adaptation of the ensemble learner to data streams
|
| OnlineCSB2 |
Online CSB2 is the online version of the ensemble learner CSB2.
|
| OnlineRUSBoost |
Online RUSBoost is the adaptation of the ensemble learner to data streams.
|
| OnlineSmoothBoost |
Incremental on-line boosting with Theoretical Justifications of Shang-Tse Chen,
Hsuan-Tien Lin and Chi-Jen Lu.
|
| OnlineSMOTEBagging |
Online SMOTEBagging is the online version of the ensemble method SMOTEBagging.
|
| OnlineUnderOverBagging |
Online UnderOverBagging is the online version of the ensemble method.
|
| Option |
Interface representing an option or parameter.
|
| OptionEditComponent |
Interface representing a component to edit an option.
|
| OptionHandler |
Interface representing an object that handles options or parameters.
|
| Options |
File option.
|
| OptionsConfigurationPanel |
This panel displays an options configuration.
|
| OptionsHandler |
|
| OptionsString |
This class get input string of learner, stream and evaluator then process them
the output will be name of learner, stream, or evaluator besides their options
|
| OrdinalParameter |
|
| ORTO |
|
| ORTO.OptionNode |
|
| OutlierAlgoPanel |
|
| OutlierEvalPanel |
|
| OutlierPanel |
|
| OutlierPerformance |
|
| OutlierSetupTab |
|
| OutlierTabPanel |
|
| OutlierVisualEvalPanel |
|
| OutlierVisualTab |
|
| OutputAttributesSelector |
|
| OzaBag |
Incremental on-line bagging of Oza and Russell.
|
| OzaBagAdwin |
Bagging for evolving data streams using ADWIN.
|
| OzaBagAdwinML |
OzaBagAdwinML: Changes the way to compute accuracy as an input for Adwin
|
| OzaBagASHT |
Bagging using trees of different size.
|
| OzaBagML |
OzaBag for Multi-label data.
|
| OzaBoost |
Incremental on-line boosting of Oza and Russell.
|
| OzaBoostAdwin |
Boosting for evolving data streams using ADWIN.
|
| PageHinkleyDM |
Drift detection method based in Page Hinkley Test.
|
| PageHinkleyFading |
|
| PageHinkleyTest |
|
| Pair<T> |
A pair of objects of the same type.
|
| Pair<T extends Comparable<T>,U extends Comparable<U>> |
|
| PairedLearners |
Creates two classifiers: a stable and a reactive.
|
| ParamGraphAxes |
ParamGraphAxes is an implementation of AbstractGraphAxes, drawing x labels
based on a parameter.
|
| ParamGraphCanvas |
ParamGraphCanvas is an implementation of AbstractGraphCanvas showing the
relation between a parameter and the measures.
|
| Pareja |
T�tulo:
|
| PartitionFunction<DATA> |
An object with partitions a set of data into two sub-sets.
|
| PartitionFunctions |
|
| PartitionFunctions.BalancedPartition<DATA> |
A partition function that tries to
distribute the data objects equally between the promoted data objects,
associating to each promoted data objects the nearest data objects. |
| PartitioningStream |
This stream partitions the base stream into n distinct streams and outputs one of them
|
| PCTWeightedICVarianceReduction |
|
| Perceptron |
Single perceptron classifier.
|
| Perceptron |
|
| Plot |
A task allowing to create and plot gnuplot scripts.
|
| Plot.LegendLocation |
Location of the legend on the plot.
|
| Plot.LegendType |
Type of legend.
|
| Plot.PlotStyle |
|
| Plot.Terminal |
Plot output terminal.
|
| PlotTab |
Generate figures plotting the performance measurements of various learning
algorithms over time.
|
| PlotTab.LegendType |
Lgend type
|
| PlotTab.PlotStyle |
Plot style
|
| PlotTab.Terminal |
Terminal
|
| Point |
|
| PointPanel |
|
| Predicate |
|
| Predicates |
|
| Prediction |
|
| Preview |
Abstract class which is used to define the methods needed from a preview
|
| PreviewCollection<CollectionElementType extends Preview> |
Class that stores and keeps the history of multiple previews
|
| PreviewCollectionLearningCurveWrapper |
Class used to wrap LearningCurve so that it can be used in
conjunction with a PreviewCollection
|
| PreviewExperiments |
|
| PreviewPanel |
This panel displays the running task preview text and buttons.
|
| PreviewPanel.TypePanel |
|
| PreviewTableModel |
Class to display the latest preview in a table
|
| ProbabilityFunction |
|
| ProcessGraphAxes |
ProcessGraphAxes is an implementation of AbstractGraphAxes, drawing x labels
based on the process frequency.
|
| ProcessGraphCanvas |
ProcessGraphCanvas is an implementation of AbstractGraphCanvas, showing one
or multiple curves over a process.
|
| PromotionFunction<DATA> |
An object that chooses a pair from a set of data objects.
|
| PromotionFunctions |
|
| PromotionFunctions.RandomPromotion<DATA> |
|
| PropertiesReader |
Class implementing some properties reader utility methods.
|
| PValuePerTwoAlgorithm |
|
| RandomAMRules |
Random AMRules algoritgm that performs analogous procedure as the Random Forest Trees but with Rules
|
| RandomAMRulesOld |
|
| RandomHoeffdingTree |
Random decision trees for data streams.
|
| RandomHoeffdingTree.LearningNodeNB |
|
| RandomHoeffdingTree.LearningNodeNBAdaptive |
|
| RandomHoeffdingTree.RandomLearningNode |
|
| RandomProjectionFilter |
Filter to perform random projection to reduce the number of attributes.
|
| RandomRBFGenerator |
Stream generator for a random radial basis function stream.
|
| RandomRBFGenerator.Centroid |
|
| RandomRBFGeneratorDrift |
Stream generator for a random radial basis function stream with drift.
|
| RandomRBFGeneratorEvents |
|
| RandomRules |
|
| RandomTreeGenerator |
Stream generator for a stream based on a randomly generated tree..
|
| RandomTreeGenerator.Node |
|
| Range |
|
| RangeOption |
Range option.
|
| RangeOptionEditComponent |
An OptionEditComponent that lets the user edit a range option.
|
| RankingGraph |
Shows the comparison of several online learning algorithms on multiple
datasets by performing appropriate statistical tests.
|
| RankPerAlgorithm |
This class contains each algorithm with its ranking.
|
| Rating |
|
| RatingPredictor |
Rating predicting algorithm.
|
| RatingPredictor |
|
| RawCellBuilder |
Implement a raw cell
|
| RBFFilter |
|
| RCD |
Creates a set of classifiers, each one representing a different context.
|
| RDDM |
|
| ReadFile |
This class processes the results files of the algorithms in each directory.
|
| RebalanceStream |
RebalanceStream
|
| RecommenderData |
|
| RecommenderData |
|
| RecurrentConceptDriftStream |
Stream generator that adds recurrent concept drifts to examples in a stream.
|
| RegressionAccuracy |
|
| RegressionMainTask |
Abstract Regression Main Task.
|
| RegressionPerformanceEvaluator |
Interface implemented by learner evaluators to monitor
the results of the regression learning process.
|
| RegressionTabPanel |
This panel allows the user to select and configure a task, and run it.
|
| RegressionTaskManagerPanel |
This panel displays the running tasks.
|
| Regressor |
Regressor interface for incremental regression models.
|
| Relation |
T�tulo:
|
| RelativeMeanAbsoluteDeviationMT |
Relative Mean Absolute Deviation for multitarget and with fading factor
|
| RelativeRootMeanSquaredErrorMT |
Relative Root Mean Squared Error for multitarget and with fading factor
|
| ReLUFilter |
|
| RemoveDiscreteAttributeFilter |
Filter for removing discrete attributes in instances of a stream.
|
| ReplacingMissingValuesFilter |
Replaces the missing values with another value according to the selected
strategy.
|
| ReplacingMissingValuesFilter.MapUtil |
|
| RequiredOptionNotSpecifiedException |
|
| ResultPreviewListener |
Interface implemented by classes that preview results
on the Graphical User Interface
|
| RootMeanSquaredError |
Computes the Root Mean Squared Error for single target regression problems
|
| RootMeanSquaredErrorMT |
Root Mean Squared Error for multitarget and with fading factor
|
| Rule |
|
| Rule.Builder |
|
| RuleActiveLearningNode |
A modified ActiveLearningNode that uses a Perceptron as the leaf node model,
and ensures that the class values sent to the attribute observers are not
truncated to ints if regression is being performed
|
| RuleActiveRegressionNode |
A modified ActiveLearningNode that uses a Perceptron as the leaf node model,
and ensures that the class values sent to the attribute observers are not
truncated to ints if regression is being performed
|
| RuleClassification |
|
| RuleClassifier |
This classifier learn ordered and unordered rule set from data stream.
|
| RuleClassifierNBayes |
This classifier learn ordered and unordered rule set from data stream with naive Bayes learners.
|
| RuleExpandedMessage |
|
| RuleSet |
|
| RuleSplitNode |
A modified SplitNode method implementing the extra information
|
| RunOutlierVisualizer |
|
| RunStreamTasks |
Task for running several experiments modifying values of parameters.
|
| RunTasks |
Task for running several experiments modifying values of parameters.
|
| RunVisualizer |
|
| SAMkNN |
Self Adjusting Memory (SAM) coupled with the k Nearest Neighbor classifier (kNN) .
|
| SamoaToWekaInstanceConverter |
The Class SamoaToWekaInstanceConverter.
|
| ScriptingTabPanel |
Tab for performing scripting via jshell.
|
| SDRSplitCriterion |
|
| SDRSplitCriterionAMRules |
|
| SDRSplitCriterionAMRulesNode |
|
| SEAGenerator |
Stream generator for SEA concepts functions.
|
| SEAGenerator.ClassFunction |
|
| SEEDChangeDetector |
Drift detection method as published in:
|
| SelectAllInputs |
Does not selects inputs
|
| SelectAllOutputs |
|
| SelectAttributesFilter |
|
| Selection |
|
| SelfOptimisingBaseTree |
See details in:
Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet.
|
| SelfOptimisingBaseTree.InnerNode |
|
| SelfOptimisingBaseTree.LeafNode |
|
| SelfOptimisingBaseTree.Node |
|
| SelfOptimisingBaseTree.SplitNode |
|
| SelfOptimisingKNearestLeaves |
Implementation of Self-Optimising K Nearest Leaves.
|
| SemiSupervisedLearner |
Learner interface for incremental semi supervised models.
|
| Separation |
|
| SeqDrift1ChangeDetector |
SeqDrift1ChangeDetector.java.
|
| SeqDrift2ChangeDetector |
SeqDriftChangeDetector.java.
|
| SerializeUtils |
Class implementing some serialize utility methods.
|
| SerializeUtils |
Class implementing some serialize utility methods.
|
| SerializeUtils.ByteCountingOutputStream |
|
| SerializeUtils.ByteCountingOutputStream |
|
| SGD |
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).
|
| SGDMultiClass |
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).
|
| SilhouetteCoefficient |
|
| SimpleBudget |
|
| SimpleCOD |
|
| SimpleCODBase |
|
| SimpleCODBase.EventItem |
|
| SimpleCODBase.EventQueue |
|
| SimpleCSVStream |
Provides a simple input stream for csv files.
|
| SineGenerator |
1.SINE1.
|
| SineGenerator.ClassFunction |
|
| SingleClassifierDrift |
Class for handling concept drift datasets with a wrapper on a
classifier.
|
| SingleVector |
Vector of float numbers with some utilities.
|
| SizeOf |
|
| SlidingMidPointOfWidestSide |
The class that splits a node into two based on the midpoint value of the dimension in which the node's rectangle is widest.
|
| SparseInstance |
The Class SparseInstance.
|
| SparseInstanceData |
The Class SparseInstanceData.
|
| SparseVector |
|
| SPegasos |
Implements the stochastic variant of the Pegasos
(Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et
al.
|
| SphereCluster |
A simple implementation of the Cluster interface representing
spherical clusters.
|
| SplitCriterion |
Interface for computing splitting criteria.
|
| SplitFunction<DATA> |
Defines an object to be used to split a node in an M-Tree.
|
| SplitFunction.SplitResult<DATA> |
|
| SSQ |
|
| StackedPredictor |
|
| STAGGERGenerator |
Stream generator for STAGGER Concept functions.
|
| STAGGERGenerator.ClassFunction |
|
| StandardisationFilter |
This filter is to standardise instances in a stream.
|
| StandardTaskMonitor |
Class that represents a standard task monitor.
|
| StatisticalCollection |
|
| StatisticalTest |
This interface represents how to perform multivariate statistical tests.
|
| StatisticalTest |
|
| Statistics |
Class implementing some distributions, tests, etc.
|
| StdDevThreshold |
|
| STEPD |
|
| STORMBase |
|
| Stream |
This class contains the name of a stream and a list of algorithms.
|
| StreamFilter |
Interface representing a stream filter.
|
| StreamingGradientBoostedTrees |
Gradient boosted trees for evolving data streams
|
| StreamingGradientBoostedTrees.SGBT |
|
| StreamingGradientBoostedTrees.SGBT.BoostingCommittee |
|
| StreamingGradientBoostedTrees.SGBT.GradHess |
|
| StreamingGradientBoostedTrees.SGBT.Objective |
|
| StreamingGradientBoostedTrees.SGBT.SoftmaxCrossEntropy |
|
| StreamingGradientBoostedTrees.SGBT.SquaredError |
|
| StreamingRandomPatches |
Streaming Random Patches
|
| StreamKM |
|
| StreamObj |
|
| StreamObj |
|
| StreamObj |
|
| StreamObj |
|
| StreamOutlierPanel |
|
| StreamPanel |
|
| StringOption |
String option.
|
| StringOptionEditComponent |
An OptionEditComponent that lets the user edit a string option.
|
| StringUtils |
Class implementing some string utility methods.
|
| StringUtils |
Class implementing some string utility methods.
|
| Summary |
This class performs the different summaries.
|
| SummaryTab |
Summarize the performance measurements of different learning algorithms over
time in LaTeX and HTML formats.
|
| SummaryTable |
Class to create the fields needed to display the summaries in the gui.
|
| SummaryViewer |
Class to display summaries in the gui.
|
| TargetMean |
|
| Task |
Interface representing a task.
|
| TaskCompletionListener |
Interface representing a listener for the task in TaskThread to be completed.
|
| TaskLauncher |
The old main class for the MOA gui, now the main class is GUI.
|
| TaskManagerForm |
|
| TaskManagerPanel |
This panel displays the running tasks.
|
| TaskManagerTabPanel |
Run online learning algorithms over multiple datasets and save the
corresponding experiment results over time: measurements of time, memory, and
predictive accuracy.
|
| TaskMonitor |
Interface representing a task monitor.
|
| TaskTextViewerPanel |
This panel displays text.
|
| TaskTextViewerPanel |
This panel displays text.
|
| TaskThread |
Task Thread.
|
| TaskThread.Status |
|
| TemporallyAugmentedClassifier |
Include labels of previous instances into the training data
|
| Test |
|
| Test |
|
| Test |
|
| Test |
|
| TestSpeed |
|
| TextGenerator |
Text generator that simulates sentiment analysis on tweets.
|
| TextViewerPanel |
This panel displays text.
|
| Timestamp |
|
| TimingUtils |
Class implementing some time utility methods.
|
| TreeCoreset |
|
| TruncatedNormal |
|
| UniformWeightedVote |
UniformWeightedVote class for weighted votes based on estimates of errors.
|
| UniformWeightedVoteMultiLabel |
UniformWeightedVote class for weighted votes based on estimates of errors.
|
| Updatable |
|
| Utils |
Class that contains several utilities
Variance
Standard deviation
Vector operations(copy, etc)
Entropy
Complementary set
|
| Utils |
Some utilities.
|
| Utils |
Class implementing some simple utility methods.
|
| VarianceRatioSplitCriterion |
|
| VarianceReductionSplitCriterion |
|
| VarianceThreshold |
|
| Vector |
|
| VFMLNumericAttributeClassObserver |
Class for observing the class data distribution for a numeric attribute as in VFML.
|
| VisualizeFeaturesPanel |
This is VisualizeFeatures tab main panel which loads data stream and shows other sub panels.
|
| VisualizeFeaturesPanel.PreprocessDefaults |
|
| Vote |
Vote class for weighted votes based on estimates of errors.
|
| VRSplitCriterion |
|
| WaveformGenerator |
Stream generator for the problem of predicting one of three waveform types.
|
| WaveformGeneratorDrift |
Stream generator for the problem of predicting one of three waveform types with drift.
|
| WeightedICVarianceReduction |
Weighted intra cluster variance reduction split criterion
|
| WeightedMajorityAlgorithm |
Weighted majority algorithm for data streams.
|
| WeightedMajorityFeatureRanking |
Weighted Majority Feature Ranking method
João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.
|
| WEKAClassifier |
Class for using a classifier from WEKA.
|
| WEKAClassOption |
WEKA class option.
|
| WEKAClassOptionEditComponent |
An OptionEditComponent that lets the user edit a WEKA class option.
|
| WekaClusteringAlgorithm |
|
| WekaExplorer |
|
| WekaToSamoaInstanceConverter |
The Class WekaToSamoaInstanceConverter.
|
| WekaUtils |
Class implementing some Weka utility methods.
|
| WindowAUCImbalancedPerformanceEvaluator |
Classification evaluator that updates evaluation results using a sliding
window.
|
| WindowClassificationPerformanceEvaluator |
Classification evaluator that updates evaluation results using a sliding
window.
|
| WindowRegressionPerformanceEvaluator |
Regression evaluator that updates evaluation results using a sliding window.
|
| WithDBSCAN |
|
| WithKmeans |
|
| WriteConfigurationToJupyterNotebook |
Export the configuration of an training method form MOA to a IPYNB file
|
| WriteMultipleStreamsToARFF |
Task to output multiple streams to a ARFF files using different random seeds
|
| WriteStreamToARFFFile |
Task to output a stream to an ARFF file
|