Uses of Interface
weka.core.Randomizable

Packages that use Randomizable

weka.classifiers
weka.classifiers.functions
weka.classifiers.meta
weka.classifiers.meta.nestedDichotomies
weka.classifiers.mi
weka.classifiers.trees
weka.clusterers
weka.core.neighboursearch.balltrees
weka.datagenerators
weka.datagenerators.classifiers.classification
weka.datagenerators.classifiers.regression
weka.datagenerators.clusterers

Uses of Randomizable in weka.classifiers

Classes in weka.classifiers that implement Randomizable

class	RandomizableClassifier Abstract utility class for handling settings common to randomizable classifiers.
class	RandomizableIteratedSingleClassifierEnhancer Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.
class	RandomizableMultipleClassifiersCombiner Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from multiple classifiers based on a given random number seed.
class	RandomizableSingleClassifierEnhancer Abstract utility class for handling settings common to randomizable meta classifiers that build an ensemble from a single base learner.

Uses of Randomizable in weka.classifiers.functions

Classes in weka.classifiers.functions that implement Randomizable

class

MultilayerPerceptron A Classifier that uses backpropagation to classify instances. This network can be built by hand, created by an algorithm or both.

Uses of Randomizable in weka.classifiers.meta

Classes in weka.classifiers.meta that implement Randomizable

class	AdaBoostM1 Class for boosting a nominal class classifier using the Adaboost M1 method.
class	Bagging Class for bagging a classifier to reduce variance.
class	CostSensitiveClassifier A metaclassifier that makes its base classifier cost-sensitive.
class	CVParameterSelection Class for performing parameter selection by cross-validation for any classifier. For more information, see: R.
class	Dagging This meta classifier creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier.
class	Decorate DECORATE is a meta-learner for building diverse ensembles of classifiers by using specially constructed artificial training examples.
class	END A meta classifier for handling multi-class datasets with 2-class classifiers by building an ensemble of nested dichotomies. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	Grading Implements Grading.
class	GridSearch Performs a grid search of parameter pairs for the a classifier (Y-axis, default is LinearRegression with the "Ridge" parameter) and the PLSFilter (X-axis, "# of Components") and chooses the best pair found for the actual predicting. The initial grid is worked on with 2-fold CV to determine the values of the parameter pairs for the selected type of evaluation (e.g., accuracy).
class	LogitBoost Class for performing additive logistic regression.
class	MetaCost This metaclassifier makes its base classifier cost-sensitive using the method specified in Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive.
class	MultiBoostAB Class for boosting a classifier using the MultiBoosting method. MultiBoosting is an extension to the highly successful AdaBoost technique for forming decision committees.
class	MultiClassClassifier A metaclassifier for handling multi-class datasets with 2-class classifiers.
class	MultiScheme Class for selecting a classifier from among several using cross validation on the training data or the performance on the training data.
class	RacedIncrementalLogitBoost Classifier for incremental learning of large datasets by way of racing logit-boosted committees. For more information see: Eibe Frank, Geoffrey Holmes, Richard Kirkby, Mark Hall: Racing committees for large datasets.
class	RandomCommittee Class for building an ensemble of randomizable base classifiers.
class	RandomSubSpace This method constructs a decision tree based classifier that maintains highest accuracy on training data and improves on generalization accuracy as it grows in complexity.
class	RotationForest Class for construction a Rotation Forest.
class	Stacking Combines several classifiers using the stacking method.
class	StackingC Implements StackingC (more efficient version of stacking). For more information, see A.K.
class	ThresholdSelector A metaclassifier that selecting a mid-point threshold on the probability output by a Classifier.
class	Vote Class for combining classifiers.

Uses of Randomizable in weka.classifiers.meta.nestedDichotomies

Classes in weka.classifiers.meta.nestedDichotomies that implement Randomizable

class	ClassBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random class-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	DataNearBalancedND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random data-balanced tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.
class	ND A meta classifier for handling multi-class datasets with 2-class classifiers by building a random tree structure. For more info, check Lin Dong, Eibe Frank, Stefan Kramer: Ensembles of Balanced Nested Dichotomies for Multi-class Problems.

Uses of Randomizable in weka.classifiers.mi

Classes in weka.classifiers.mi that implement Randomizable

class

MIEMDD EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm. It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.

Uses of Randomizable in weka.classifiers.trees

Classes in weka.classifiers.trees that implement Randomizable

class	BFTree Class for building a best-first decision tree classifier.
class	RandomForest Class for constructing a forest of random trees. For more information see: Leo Breiman (2001).
class	RandomTree Class for constructing a tree that considers K randomly chosen attributes at each node.
class	SimpleCart Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split method. For more information, see: Leo Breiman, Jerome H.

Uses of Randomizable in weka.clusterers

Classes in weka.clusterers that implement Randomizable

class	Cobweb Class implementing the Cobweb and Classit clustering algorithms. Note: the application of node operators (merging, splitting etc.) in terms of ordering and priority differs (and is somewhat ambiguous) between the original Cobweb and Classit papers.
class	EM Simple EM (expectation maximisation) class. EM assigns a probability distribution to each instance which indicates the probability of it belonging to each of the clusters.
class	FarthestFirst Cluster data using the FarthestFirst algorithm. For more information see: Hochbaum, Shmoys (1985).
class	RandomizableClusterer Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableDensityBasedClusterer Abstract utility class for handling settings common to randomizable clusterers.
class	RandomizableSingleClustererEnhancer Abstract utility class for handling settings common to randomizable clusterers.
class	sIB Cluster data using the sequential information bottleneck algorithm. Note: only hard clustering scheme is supported.
class	SimpleKMeans Cluster data using the k means algorithm Valid options are:
class	XMeans Cluster data using the X-means algorithm. X-Means is K-Means extended by an Improve-Structure part In this part of the algorithm the centers are attempted to be split in its region.

Uses of Randomizable in weka.core.neighboursearch.balltrees

Classes in weka.core.neighboursearch.balltrees that implement Randomizable

class

MiddleOutConstructor The class that builds a BallTree middle out. For more information see also: Andrew W.

Uses of Randomizable in weka.datagenerators

Classes in weka.datagenerators that implement Randomizable

class	ClassificationGenerator Abstract class for data generators for classifiers.
class	ClusterGenerator Abstract class for cluster data generators.
class	DataGenerator Abstract superclass for data generators that generate data for classifiers and clusterers.
class	RegressionGenerator Abstract class for data generators for regression classifiers.

Uses of Randomizable in weka.datagenerators.classifiers.classification

Classes in weka.datagenerators.classifiers.classification that implement Randomizable

class	Agrawal Generates a people database and is based on the paper by Agrawal et al.: R.
class	BayesNet Generates random instances based on a Bayes network.
class	LED24 This generator produces data for a display with 7 LEDs.
class	RandomRBF RandomRBF data is generated by first creating a random set of centers for each class.
class	RDG1 A data generator that produces data randomly by producing a decision list. The decision list consists of rules. Instances are generated randomly one by one.

Uses of Randomizable in weka.datagenerators.classifiers.regression

Classes in weka.datagenerators.classifiers.regression that implement Randomizable

class	Expression A data generator for generating y according to a given expression out of randomly generated x. E.g., the mexican hat can be generated like this: sin(abs(a1)) / abs(a1) In addition to this function, the amplitude can be changed and gaussian noise can be added.
class	MexicanHat A data generator for the simple 'Mexian Hat' function: y = sin|x| / |x| In addition to this simple function, the amplitude can be changed and gaussian noise can be added.

Uses of Randomizable in weka.datagenerators.clusterers

Classes in weka.datagenerators.clusterers that implement Randomizable

class	BIRCHCluster Cluster data generator designed for the BIRCH System Dataset is generated with instances in K clusters. Instances are 2-d data points. Each cluster is characterized by the number of data points in itits radius and its center.
class	SubspaceCluster A data generator that produces data points in hyperrectangular subspace clusters.