Uses of Interface
moa.capabilities.CapabilitiesHandler
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Uses of CapabilitiesHandler in moa.capabilities
Methods in moa.capabilities with parameters of type CapabilitiesHandler Modifier and Type Method Description boolean
CapabilityRequirement. isMetBy(CapabilitiesHandler handler)
Tests if the requirement is met by the given capabilities handler. -
Uses of CapabilitiesHandler in moa.classifiers
Classes in moa.classifiers that implement CapabilitiesHandler Modifier and Type Class Description class
AbstractClassifier
class
AbstractMultiLabelLearner
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Uses of CapabilitiesHandler in moa.classifiers.active
Classes in moa.classifiers.active that implement CapabilitiesHandler Modifier and Type Class Description class
ALRandom
class
ALUncertainty
Active learning setting for evolving data streams. -
Uses of CapabilitiesHandler in moa.classifiers.bayes
Classes in moa.classifiers.bayes that implement CapabilitiesHandler Modifier and Type Class Description class
NaiveBayes
Naive Bayes incremental learner.class
NaiveBayesMultinomial
Class for building and using a multinomial Naive Bayes classifier. -
Uses of CapabilitiesHandler in moa.classifiers.deeplearning
Classes in moa.classifiers.deeplearning that implement CapabilitiesHandler Modifier and Type Class Description class
CAND
Continuously Adaptive Neural networks for Data streamsclass
MLP
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Uses of CapabilitiesHandler in moa.classifiers.drift
Classes in moa.classifiers.drift that implement CapabilitiesHandler Modifier and Type Class Description class
DriftDetectionMethodClassifier
Class for handling concept drift datasets with a wrapper on a classifier.class
SingleClassifierDrift
Class for handling concept drift datasets with a wrapper on a classifier. -
Uses of CapabilitiesHandler in moa.classifiers.functions
Classes in moa.classifiers.functions that implement CapabilitiesHandler Modifier and Type Class Description class
AdaGrad
Implements the AdaGrad oneline optimiser for learning various linear models (binary class SVM, binary class logistic regression and linear regression).class
MajorityClass
Majority class learner.class
NoChange
NoChange class classifier.class
Perceptron
Single perceptron classifier.class
SGD
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).class
SGDMultiClass
Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression and linear regression).class
SPegasos
Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. -
Uses of CapabilitiesHandler in moa.classifiers.lazy
Classes in moa.classifiers.lazy that implement CapabilitiesHandler Modifier and Type Class Description class
kNN
k Nearest Neighbor.class
kNNwithPAW
k Nearest Neighbor ADAPTIVE with PAW.class
kNNwithPAWandADWIN
k Nearest Neighbor ADAPTIVE with ADWIN+PAW.class
SAMkNN
Self Adjusting Memory (SAM) coupled with the k Nearest Neighbor classifier (kNN) . -
Uses of CapabilitiesHandler in moa.classifiers.meta
Classes in moa.classifiers.meta that implement CapabilitiesHandler Modifier and Type Class Description class
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.class
AccuracyWeightedEnsemble
The Accuracy Weighted Ensemble classifier as proposed by Wang et al.class
ADACC
Anticipative and Dynamic Adaptation to Concept Changes.class
AdaptiveRandomForest
Adaptive Random Forestclass
AdaptiveRandomForestRegressor
Implementation of AdaptiveRandomForestRegressor, an extension of AdaptiveRandomForest for classification.class
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.class
BOLE
class
DACC
Dynamic Adaptation to Concept Changes.class
DynamicWeightedMajority
Dynamic weighted majority algorithm.class
HeterogeneousEnsembleAbstract
BLAST (Best Last) for Heterogeneous Ensembles Abstract Base Classclass
HeterogeneousEnsembleBlast
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factorsclass
HeterogeneousEnsembleBlastFadingFactors
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factorsclass
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.class
LeveragingBag
Leveraging Bagging for evolving data streams using ADWIN.class
LimAttClassifier
Ensemble Combining Restricted Hoeffding Trees using Stacking.class
MLCviaMTR
class
OCBoost
Online Coordinate boosting for two classes evolving data streams.class
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.class
OnlineSmoothBoost
Incremental on-line boosting with Theoretical Justifications of Shang-Tse Chen, Hsuan-Tien Lin and Chi-Jen Lu.class
OzaBag
Incremental on-line bagging of Oza and Russell.class
OzaBagAdwin
Bagging for evolving data streams using ADWIN.class
OzaBagASHT
Bagging using trees of different size.class
OzaBoost
Incremental on-line boosting of Oza and Russell.class
OzaBoostAdwin
Boosting for evolving data streams using ADWIN.class
PairedLearners
Creates two classifiers: a stable and a reactive.class
RandomRules
class
RCD
Creates a set of classifiers, each one representing a different context.class
SelfOptimisingKNearestLeaves
Implementation of Self-Optimising K Nearest Leaves.class
StreamingGradientBoostedTrees
Gradient boosted trees for evolving data streamsclass
StreamingRandomPatches
Streaming Random Patchesclass
TemporallyAugmentedClassifier
Include labels of previous instances into the training dataclass
WeightedMajorityAlgorithm
Weighted majority algorithm for data streams.class
WEKAClassifier
Class for using a classifier from WEKA. -
Uses of CapabilitiesHandler in moa.classifiers.meta.imbalanced
Classes in moa.classifiers.meta.imbalanced that implement CapabilitiesHandler Modifier and Type Class Description class
CSMOTE
CSMOTEclass
OnlineAdaBoost
Online AdaBoost is the online version of the boosting ensemble method AdaBoostclass
OnlineAdaC2
OnlineAdaC2 is the adaptation of the ensemble learner to data streamsclass
OnlineCSB2
Online CSB2 is the online version of the ensemble learner CSB2.class
OnlineRUSBoost
Online RUSBoost is the adaptation of the ensemble learner to data streams.class
OnlineSMOTEBagging
Online SMOTEBagging is the online version of the ensemble method SMOTEBagging.class
OnlineUnderOverBagging
Online UnderOverBagging is the online version of the ensemble method.class
RebalanceStream
RebalanceStream -
Uses of CapabilitiesHandler in moa.classifiers.multilabel
Classes in moa.classifiers.multilabel that implement CapabilitiesHandler Modifier and Type Class Description class
MajorityLabelset
Majority Labelset classifier.class
MEKAClassifier
Wrapper for MEKA classifiers.class
MultilabelHoeffdingTree
Hoeffding Tree for classifying multi-label data. -
Uses of CapabilitiesHandler in moa.classifiers.multilabel.meta
Classes in moa.classifiers.multilabel.meta that implement CapabilitiesHandler Modifier and Type Class Description class
OzaBagAdwinML
OzaBagAdwinML: Changes the way to compute accuracy as an input for Adwinclass
OzaBagML
OzaBag for Multi-label data. -
Uses of CapabilitiesHandler in moa.classifiers.multilabel.trees
Classes in moa.classifiers.multilabel.trees that implement CapabilitiesHandler Modifier and Type Class Description class
ISOUPTree
iSOUPTree class for structured output prediction.class
ISOUPTreeRF
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Uses of CapabilitiesHandler in moa.classifiers.multitarget
Classes in moa.classifiers.multitarget that implement CapabilitiesHandler Modifier and Type Class Description class
BasicMultiLabelClassifier
class
BasicMultiLabelLearner
Binary relevance Multilabel Classifierclass
BasicMultiTargetRegressor
Binary relevance Multi-Target Regressor -
Uses of CapabilitiesHandler in moa.classifiers.multitarget.functions
Classes in moa.classifiers.multitarget.functions that implement CapabilitiesHandler Modifier and Type Class Description class
MultiTargetNoChange
MultiTargetNoChange class regressor. -
Uses of CapabilitiesHandler in moa.classifiers.oneclass
Classes in moa.classifiers.oneclass that implement CapabilitiesHandler Modifier and Type Class Description class
Autoencoder
Implements an autoencoder: a neural network that attempts to reconstruct the input.class
HSTrees
Implements the Streaming Half-Space Trees one-class classifier described in S.class
NearestNeighbourDescription
Implements David Tax's Nearest Neighbour Description method described in Section 3.4.2 of D. -
Uses of CapabilitiesHandler in moa.classifiers.rules
Classes in moa.classifiers.rules that implement CapabilitiesHandler Modifier and Type Class Description class
AbstractAMRules
class
AMRulesRegressor
class
AMRulesRegressorOld
class
BinaryClassifierFromRegressor
Function that convertes a regressor into a binary classifier baseLearnerOption- regressor learner selectionclass
RuleClassifier
This classifier learn ordered and unordered rule set from data stream.class
RuleClassifierNBayes
This classifier learn ordered and unordered rule set from data stream with naive Bayes learners. -
Uses of CapabilitiesHandler in moa.classifiers.rules.functions
Classes in moa.classifiers.rules.functions that implement CapabilitiesHandler Modifier and Type Class Description class
AdaptiveNodePredictor
class
FadingTargetMean
class
LowPassFilteredLearner
class
Perceptron
class
TargetMean
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Uses of CapabilitiesHandler in moa.classifiers.rules.meta
Classes in moa.classifiers.rules.meta that implement CapabilitiesHandler Modifier and Type Class Description class
RandomAMRules
Random AMRules algoritgm that performs analogous procedure as the Random Forest Trees but with Rulesclass
RandomAMRulesOld
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Uses of CapabilitiesHandler in moa.classifiers.rules.multilabel
Classes in moa.classifiers.rules.multilabel that implement CapabilitiesHandler Modifier and Type Class Description class
AMRulesMultiLabelClassifier
Method for online multi-Label classification.class
AMRulesMultiLabelLearner
Adaptive Model Rules for MultiLabel problems (AMRulesML), the streaming rule learning algorithm.class
AMRulesMultiLabelLearnerSemiSuper
Semi-supervised method for online multi-target regression.class
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.class
AMRulesMultiTargetRegressorSemiSuper
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Uses of CapabilitiesHandler in moa.classifiers.rules.multilabel.functions
Classes in moa.classifiers.rules.multilabel.functions that implement CapabilitiesHandler Modifier and Type Class Description class
AbstractAMRulesFunctionBasicMlLearner
class
AdaptiveMultiTargetRegressor
Adaptive MultiTarget Regressor uses two learner The first is used in first stage when high error are produced(e.g.class
DominantLabelsClassifier
class
MultiLabelNaiveBayes
Binary relevance with Naive Bayesclass
MultiLabelPerceptronClassification
Multi-Label perceptron classifier (by Binary Relevance).class
MultiTargetMeanRegressor
Target mean regressorclass
MultiTargetPerceptronRegressor
Binary relevance with a regression perceptronclass
StackedPredictor
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Uses of CapabilitiesHandler in moa.classifiers.rules.multilabel.meta
Classes in moa.classifiers.rules.multilabel.meta that implement CapabilitiesHandler Modifier and Type Class Description class
MultiLabelRandomAMRules
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Uses of CapabilitiesHandler in moa.classifiers.trees
Classes in moa.classifiers.trees that implement CapabilitiesHandler Modifier and Type Class Description class
AdaHoeffdingOptionTree
Adaptive decision option tree for streaming data with adaptive Naive Bayes classification at leaves.class
ARFFIMTDD
Implementation of ARFFIMTDD, an extension of FIMTDD to be used by AdaptiveRandomForestRegressor.class
ARFHoeffdingTree
Adaptive Random Forest Hoeffding Tree.class
ASHoeffdingTree
Adaptive Size Hoeffding Tree used in Bagging using trees of different size.class
DecisionStump
Decision trees of one level.
Parameters:class
EFDT
class
FIMTDD
Implementation of FIMTDD, regression and model trees for data streams.class
HoeffdingAdaptiveTree
Hoeffding Adaptive Tree for evolving data streams.class
HoeffdingAdaptiveTreeClassifLeaves
Hoeffding Adaptive Tree for evolving data streams that has a classifier at the leaves.class
HoeffdingOptionTree
Hoeffding Option Tree.class
HoeffdingTree
Hoeffding Tree or VFDT.class
HoeffdingTreeClassifLeaves
Hoeffding Tree that have a classifier at the leaves.class
LimAttHoeffdingTree
Hoeffding decision trees with a restricted number of attributes for data streams.class
ORTO
class
RandomHoeffdingTree
Random decision trees for data streams.class
SelfOptimisingBaseTree
See details in:
Yibin Sun, Bernhard Pfahringer, Heitor Murilo Gomes, Albert Bifet. -
Uses of CapabilitiesHandler in moa.classifiers.trees.iadem
Classes in moa.classifiers.trees.iadem that implement CapabilitiesHandler Modifier and Type Class Description class
Iadem2
class
Iadem3
class
Iadem3Subtree
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Uses of CapabilitiesHandler in moa.evaluation
Subinterfaces of CapabilitiesHandler in moa.evaluation Modifier and Type Interface Description interface
ALClassificationPerformanceEvaluator
Active Learning Evaluator Interface to make AL Evaluators selectable in AL tasks.interface
ClassificationPerformanceEvaluator
interface
LearningPerformanceEvaluator<E extends Example>
Interface implemented by learner evaluators to monitor the results of the learning process.interface
MultiLabelPerformanceEvaluator
Interface implemented by learner evaluators to monitor the results of the regression learning process.interface
MultiTargetPerformanceEvaluator
Interface implemented by learner evaluators to monitor the results of the regression learning process.interface
RegressionPerformanceEvaluator
Interface implemented by learner evaluators to monitor the results of the regression learning process.Classes in moa.evaluation that implement CapabilitiesHandler Modifier and Type Class Description class
AdwinClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an adaptive sliding window.class
ALWindowClassificationPerformanceEvaluator
Active Learning Wrapper for BasicClassificationPerformanceEvaluator.class
BasicAUCImbalancedPerformanceEvaluator
Performance measures designed for class imbalance problems.class
BasicClassificationPerformanceEvaluator
Classification evaluator that performs basic incremental evaluation.class
BasicConceptDriftPerformanceEvaluator
class
BasicMultiLabelPerformanceEvaluator
Multilabel Window Classification Performance Evaluator.class
BasicMultiTargetPerformanceEvaluator
Regression evaluator that performs basic incremental evaluation.class
BasicMultiTargetPerformanceRelativeMeasuresEvaluator
Regression evaluator that performs basic incremental evaluation.class
BasicRegressionPerformanceEvaluator
Regression evaluator that performs basic incremental evaluation.class
EWMAClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an Exponential Weighted Moving Average.class
FadingFactorClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using a fading factor.class
MultiTargetWindowRegressionPerformanceEvaluator
Multi-target regression evaluator that updates evaluation results using a sliding window.class
MultiTargetWindowRegressionPerformanceRelativeMeasuresEvaluator
Multi-target regression evaluator that updates evaluation results using a sliding window.class
WindowAUCImbalancedPerformanceEvaluator
Classification evaluator that updates evaluation results using a sliding window.class
WindowClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using a sliding window.class
WindowRegressionPerformanceEvaluator
Regression evaluator that updates evaluation results using a sliding window. -
Uses of CapabilitiesHandler in moa.learners
Classes in moa.learners that implement CapabilitiesHandler Modifier and Type Class Description class
ChangeDetectorLearner
Class for detecting concept drift and to be used as a learner. -
Uses of CapabilitiesHandler in moa.learners.featureanalysis
Classes in moa.learners.featureanalysis that implement CapabilitiesHandler Modifier and Type Class Description class
ClassifierWithFeatureImportance
Classifier with Feature Importanceclass
FeatureImportanceHoeffdingTree
HoeffdingTree Feature Importance extends the traditional HoeffdingTree classifier to also yield feature importances.class
FeatureImportanceHoeffdingTreeEnsemble
HoeffdingTree Ensemble Feature Importance. -
Uses of CapabilitiesHandler in moa.streams
Subinterfaces of CapabilitiesHandler in moa.streams Modifier and Type Interface Description interface
ExampleStream<E extends Example>
Interface representing a data stream of examples.interface
InstanceStream
Interface representing a data stream of instances.interface
MultiTargetInstanceStream
Interface representing a data stream of instances.Classes in moa.streams that implement CapabilitiesHandler Modifier and Type Class Description class
ArffFileStream
Stream reader of ARFF files.class
BootstrappedStream
Bootstrapped Streamclass
CachedInstancesStream
Stream generator for representing a stream that is cached in memory.class
ConceptDriftRealStream
Stream generator that adds concept drift to examples in a stream with different classes and attributes.class
ConceptDriftStream
Stream generator that adds concept drift to examples in a stream.class
FilteredStream
Class for representing a stream that is filtered.class
ImbalancedStream
Imbalanced Stream.class
IrrelevantFeatureAppenderStream
IrrelevantFeatureAppender Stream.class
MultiFilteredStream
Class for representing a stream that is filtered.class
MultiLabelFilteredStream
Class for representing a stream that is filtered.class
MultiTargetArffFileStream
Stream reader of ARFF files.class
PartitioningStream
This stream partitions the base stream into n distinct streams and outputs one of themclass
RecurrentConceptDriftStream
Stream generator that adds recurrent concept drifts to examples in a stream. -
Uses of CapabilitiesHandler in moa.streams.clustering
Classes in moa.streams.clustering that implement CapabilitiesHandler Modifier and Type Class Description class
ClusteringStream
class
FileStream
class
RandomRBFGeneratorEvents
class
SimpleCSVStream
Provides a simple input stream for csv files. -
Uses of CapabilitiesHandler in moa.streams.filters
Subinterfaces of CapabilitiesHandler in moa.streams.filters Modifier and Type Interface Description interface
MultiLabelStreamFilter
interface
StreamFilter
Interface representing a stream filter.Classes in moa.streams.filters that implement CapabilitiesHandler Modifier and Type Class Description class
AbstractMultiLabelStreamFilter
Abstract Stream Filter.class
AbstractStreamFilter
Abstract Stream Filter.class
AddNoiseFilter
Filter for adding random noise to examples in a stream.class
HashingTrickFilter
Filter to perform feature hashing to reduce the number of attributes by applying a hash function to features.class
NormalisationFilter
Filter for standardising and normalising instances in a stream.class
RandomProjectionFilter
Filter to perform random projection to reduce the number of attributes.class
RBFFilter
class
ReLUFilter
class
RemoveDiscreteAttributeFilter
Filter for removing discrete attributes in instances of a stream.class
ReplacingMissingValuesFilter
Replaces the missing values with another value according to the selected strategy.class
SelectAttributesFilter
class
StandardisationFilter
This filter is to standardise instances in a stream. -
Uses of CapabilitiesHandler in moa.streams.generators
Classes in moa.streams.generators that implement CapabilitiesHandler Modifier and Type Class Description class
AgrawalGenerator
Stream generator for Agrawal dataset.class
AssetNegotiationGenerator
class
HyperplaneGenerator
Stream generator for Hyperplane data stream.class
LEDGenerator
Stream generator for the problem of predicting the digit displayed on a 7-segment LED display.class
LEDGeneratorDrift
Stream generator for the problem of predicting the digit displayed on a 7-segment LED display with drift.class
MixedGenerator
Abrupt concept drift, boolean noise-free examples.class
RandomRBFGenerator
Stream generator for a random radial basis function stream.class
RandomRBFGeneratorDrift
Stream generator for a random radial basis function stream with drift.class
RandomTreeGenerator
Stream generator for a stream based on a randomly generated tree..class
SEAGenerator
Stream generator for SEA concepts functions.class
SineGenerator
1.SINE1.class
STAGGERGenerator
Stream generator for STAGGER Concept functions.class
TextGenerator
Text generator that simulates sentiment analysis on tweets.class
WaveformGenerator
Stream generator for the problem of predicting one of three waveform types.class
WaveformGeneratorDrift
Stream generator for the problem of predicting one of three waveform types with drift. -
Uses of CapabilitiesHandler in moa.streams.generators.cd
Subinterfaces of CapabilitiesHandler in moa.streams.generators.cd Modifier and Type Interface Description interface
ConceptDriftGenerator
Classes in moa.streams.generators.cd that implement CapabilitiesHandler Modifier and Type Class Description class
AbruptChangeGenerator
class
AbstractConceptDriftGenerator
class
GradualChangeGenerator
class
NoChangeGenerator
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Uses of CapabilitiesHandler in moa.streams.generators.multilabel
Classes in moa.streams.generators.multilabel that implement CapabilitiesHandler Modifier and Type Class Description class
MetaMultilabelGenerator
Stream generator for multilabel data.class
MultilabelArffFileStream
Stream reader for ARFF files of multilabel data. -
Uses of CapabilitiesHandler in moa.tasks
Classes in moa.tasks that implement CapabilitiesHandler Modifier and Type Class Description class
ClassificationMainTask
Abstract Classification Main Task.class
EvaluateInterleavedChunks
class
EvaluateInterleavedTestThenTrain
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
EvaluateModel
Task for evaluating a static model on a stream.class
EvaluatePeriodicHeldOutTest
Task for evaluating a classifier on a stream by periodically testing on a heldout set.class
EvaluatePrequential
Task for evaluating a classifier on a stream by testing then training with each example in sequence.class
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.class
EvaluatePrequentialDelayed
Task for evaluating a classifier on a delayed stream by testing and only training with the example after k other examples (delayed labeling).class
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).class
FeatureImportanceConfig
This class Provides GUI to user so that they can configure parameters for feature importance algorithm.class
LearnModel
Task for learning a model without any evaluation.
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