Modifier and Type | Interface and Description |
---|---|
interface |
MultiLabelClassifier |
interface |
MultiLabelLearner |
interface |
MultiTargetLearnerSemiSupervised |
interface |
MultiTargetRegressor
MultiTargetRegressor interface for incremental MultiTarget regression models.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractClassifier |
class |
AbstractMultiLabelLearner |
Modifier and Type | Method and Description |
---|---|
Classifier |
Classifier.copy()
Produces a copy of this learner.
|
Classifier |
AbstractClassifier.copy() |
Classifier[] |
Classifier.getSubClassifiers()
Gets the classifiers of this ensemble.
|
Classifier[] |
AbstractClassifier.getSubClassifiers() |
Modifier and Type | Interface and Description |
---|---|
interface |
ALClassifier
Active Learning Classifier Interface to make AL Classifiers selectable in AL tasks.
|
Modifier and Type | Class and Description |
---|---|
class |
ALRandom |
class |
ALUncertainty
Active learning setting for evolving data streams.
|
Modifier and Type | Field and Description |
---|---|
Classifier |
ALUncertainty.classifier |
Classifier |
ALRandom.classifier |
Modifier and Type | Class and Description |
---|---|
class |
NaiveBayes
Naive Bayes incremental learner.
|
class |
NaiveBayesMultinomial
Class for building and using a multinomial Naive
Bayes classifier.
|
Modifier and Type | Class and 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.
|
Modifier and Type | Field and Description |
---|---|
protected Classifier |
DriftDetectionMethodClassifier.classifier |
protected Classifier |
DriftDetectionMethodClassifier.newclassifier |
Modifier and Type | Class and 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 |
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.
|
Modifier and Type | Class and 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) .
|
Modifier and Type | Class and 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 Forest
|
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 Class
|
class |
HeterogeneousEnsembleBlast
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factors
|
class |
HeterogeneousEnsembleBlastFadingFactors
BLAST (Best Last) for Heterogeneous Ensembles implemented with Fading Factors
|
class |
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 |
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 |
TemporallyAugmentedClassifier
Include labels of previous instances into the training data
|
class |
WeightedMajorityAlgorithm
Weighted majority algorithm for data streams.
|
class |
WEKAClassifier
Class for using a classifier from WEKA.
|
Modifier and Type | Field and Description |
---|---|
protected Classifier |
TemporallyAugmentedClassifier.baseLearner |
protected Classifier |
AccuracyUpdatedEnsemble.candidate
Candidate classifier.
|
protected Classifier |
AccuracyWeightedEnsemble.candidateClassifier |
protected Classifier[] |
LeveragingBag.ensemble |
protected Classifier[] |
LimAttClassifier.ensemble |
protected Classifier[] |
OzaBagAdwin.ensemble |
protected Classifier[] |
OnlineSmoothBoost.ensemble |
protected Classifier[] |
OzaBag.ensemble |
protected Classifier[] |
HeterogeneousEnsembleAbstract.ensemble |
protected Classifier[] |
ADOB.ensemble |
protected Classifier[] |
OzaBoost.ensemble |
protected Classifier[] |
AccuracyWeightedEnsemble.ensemble |
protected Classifier[] |
RandomRules.ensemble |
protected Classifier[] |
BOLE.ensemble |
protected Classifier[] |
WeightedMajorityAlgorithm.ensemble |
protected Classifier[] |
DACC.ensemble
Ensemble of classifiers
|
protected Classifier[] |
OzaBoostAdwin.ensemble |
protected Classifier[] |
OCBoost.ensemble |
protected Classifier[] |
AccuracyUpdatedEnsemble.learners
Ensemble classifiers.
|
protected Classifier |
PairedLearners.reactiveLearner |
protected Classifier |
PairedLearners.stableLearner |
protected Classifier[] |
AccuracyWeightedEnsemble.storedLearners |
Modifier and Type | Field and Description |
---|---|
protected List<Classifier> |
LearnNSE.ensemble |
protected List<Classifier> |
DynamicWeightedMajority.experts |
Modifier and Type | Method and Description |
---|---|
protected Classifier |
AccuracyWeightedEnsemble.addToStored(Classifier newClassifier,
double newClassifiersWeight)
Adds a classifier to the storage.
|
protected Classifier |
AccuracyUpdatedEnsemble.addToStored(Classifier newClassifier,
double newClassifiersWeight)
Adds a classifier to the storage.
|
Classifier[] |
LeveragingBag.getSubClassifiers() |
Classifier[] |
LimAttClassifier.getSubClassifiers() |
Classifier[] |
OzaBagAdwin.getSubClassifiers() |
Classifier[] |
OnlineSmoothBoost.getSubClassifiers() |
Classifier[] |
OzaBag.getSubClassifiers() |
Classifier[] |
ADOB.getSubClassifiers() |
Classifier[] |
OzaBoost.getSubClassifiers() |
Classifier[] |
AccuracyWeightedEnsemble.getSubClassifiers() |
Classifier[] |
RandomRules.getSubClassifiers() |
Classifier[] |
AdaptiveRandomForest.getSubClassifiers() |
Classifier[] |
BOLE.getSubClassifiers() |
Classifier[] |
OnlineAccuracyUpdatedEnsemble.getSubClassifiers() |
Classifier[] |
WeightedMajorityAlgorithm.getSubClassifiers() |
Classifier[] |
DACC.getSubClassifiers() |
Classifier[] |
AccuracyUpdatedEnsemble.getSubClassifiers() |
Classifier[] |
OzaBoostAdwin.getSubClassifiers() |
Classifier[] |
OzaBagASHT.getSubClassifiers() |
Classifier[] |
OCBoost.getSubClassifiers() |
Modifier and Type | Method and Description |
---|---|
protected Classifier |
AccuracyWeightedEnsemble.addToStored(Classifier newClassifier,
double newClassifiersWeight)
Adds a classifier to the storage.
|
protected Classifier |
AccuracyUpdatedEnsemble.addToStored(Classifier newClassifier,
double newClassifiersWeight)
Adds a classifier to the storage.
|
protected double |
AccuracyWeightedEnsemble.computeCandidateWeight(Classifier candidate,
Instances chunk,
int numFolds)
Computes the weight of a candidate classifier.
|
protected double |
AccuracyUpdatedEnsemble.computeMse(Classifier learner,
Instances chunk)
Computes the MSE of a learner for a given chunk of examples.
|
protected double |
AccuracyWeightedEnsemble.computeWeight(Classifier learner,
Instances chunk)
Computes the weight of a given classifie.
|
Constructor and Description |
---|
ClassifierWithMemory(Classifier classifier,
int windowSize) |
DetectingRunnable(Classifier learner,
ADWIN ADError,
Instance inst) |
TrainingRunnable(Classifier learner,
Instance weightedInst) |
TrainingRunnable(Classifier learner,
Instance weightedInst) |
Modifier and Type | Class and Description |
---|---|
class |
MajorityLabelset
Majority Labelset classifier.
|
class |
MEKAClassifier
Wrapper for MEKA classifiers.
|
class |
MultilabelHoeffdingTree
Hoeffding Tree for classifying multi-label data.
|
Modifier and Type | Method and Description |
---|---|
Classifier |
MultilabelHoeffdingTree.MultilabelLearningNodeClassifier.getClassifier() |
Modifier and Type | Method and Description |
---|---|
protected HoeffdingTree.LearningNode |
MultilabelHoeffdingTree.newLearningNode(double[] initialClassObservations,
Classifier cl) |
Constructor and Description |
---|
MultilabelLearningNodeClassifier(double[] initialClassObservations,
Classifier cl,
MultilabelHoeffdingTree ht) |
Modifier and Type | Class and Description |
---|---|
class |
OzaBagAdwinML
OzaBagAdwinML: Changes the way to compute accuracy as an input for Adwin
|
class |
OzaBagML
OzaBag for Multi-label data.
|
Modifier and Type | Method and Description |
---|---|
static Prediction |
OzaBagML.compilePredictions(Classifier[] h,
Example example) |
static double[] |
OzaBagML.compileVotes(Classifier[] h,
Instance inst) |
Modifier and Type | Class and Description |
---|---|
class |
ISOUPTree
iSOUPTrees class for structured output prediction.
|
Modifier and Type | Class and Description |
---|---|
class |
BasicMultiLabelClassifier |
class |
BasicMultiLabelLearner
Binary relevance Multilabel Classifier
|
class |
BasicMultiTargetRegressor
Binary relevance Multi-Target Regressor
|
Modifier and Type | Field and Description |
---|---|
protected Classifier[] |
BasicMultiTargetRegressor.ensemble |
protected Classifier[] |
BasicMultiLabelLearner.ensemble |
Modifier and Type | Class and Description |
---|---|
class |
MultiTargetNoChange
MultiTargetNoChange class regressor.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractAMRules |
class |
AMRulesRegressor |
class |
AMRulesRegressorOld |
class |
BinaryClassifierFromRegressor
Function that convertes a regressor into a binary classifier
baseLearnerOption- regressor learner selection
|
class |
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.
|
Modifier and Type | Class and Description |
---|---|
class |
AdaptiveNodePredictor |
class |
FadingTargetMean |
class |
LowPassFilteredLearner |
class |
Perceptron |
class |
TargetMean |
Modifier and Type | Class and Description |
---|---|
class |
RandomAMRules
Random AMRules algoritgm that performs analogous procedure as the Random Forest Trees but with Rules
|
class |
RandomAMRulesOld |
Modifier and Type | Method and Description |
---|---|
Classifier[] |
RandomAMRulesOld.getSubClassifiers() |
Modifier and Type | Class and 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 |
Modifier and Type | Class and 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 Bayes
|
class |
MultiLabelPerceptronClassification
Multi-Label perceptron classifier (by Binary Relevance).
|
class |
MultiTargetMeanRegressor
Target mean regressor
|
class |
MultiTargetPerceptronRegressor
Binary relevance with a regression perceptron
|
class |
StackedPredictor |
Modifier and Type | Class and Description |
---|---|
class |
MultiLabelRandomAMRules |
Modifier and Type | Class and Description |
---|---|
class |
AdaHoeffdingOptionTree
Adaptive decision option tree for streaming data with adaptive Naive
Bayes classification at leaves.
|
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 |
FIMTDD |
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.
|
Modifier and Type | Field and Description |
---|---|
protected Classifier |
HoeffdingTreeClassifLeaves.LearningNodeClassifier.classifier |
protected Classifier |
HoeffdingAdaptiveTreeClassifLeaves.LearningNodeHATClassifier.classifier |
Modifier and Type | Method and Description |
---|---|
Classifier |
HoeffdingTreeClassifLeaves.LearningNodeClassifier.getClassifier() |
Classifier |
HoeffdingAdaptiveTreeClassifLeaves.LearningNodeHATClassifier.getClassifier() |
Modifier and Type | Method and Description |
---|---|
protected HoeffdingTree.LearningNode |
HoeffdingTreeClassifLeaves.newLearningNode(double[] initialClassObservations,
Classifier cl) |
protected HoeffdingTree.LearningNode |
HoeffdingAdaptiveTreeClassifLeaves.newLearningNode(double[] initialClassObservations,
Classifier cl) |
Constructor and Description |
---|
LearningNodeClassifier(double[] initialClassObservations,
Classifier cl,
HoeffdingTreeClassifLeaves ht) |
LearningNodeHATClassifier(double[] initialClassObservations,
Classifier cl,
HoeffdingAdaptiveTreeClassifLeaves ht) |
Modifier and Type | Class and Description |
---|---|
class |
Iadem2 |
class |
Iadem3 |
class |
Iadem3Subtree |
Modifier and Type | Class and Description |
---|---|
class |
ChangeDetectorLearner
Class for detecting concept drift and to be used as a learner.
|
Constructor and Description |
---|
EvaluateModel(Classifier model,
InstanceStream stream,
LearningPerformanceEvaluator evaluator,
int maxInstances) |
EvaluateModelMultiLabel(Classifier model,
InstanceStream stream,
LearningPerformanceEvaluator evaluator,
int maxInstances) |
EvaluateModelMultiTarget(Classifier model,
InstanceStream stream,
LearningPerformanceEvaluator evaluator,
int maxInstances) |
EvaluateModelRegression(Classifier model,
InstanceStream stream,
LearningPerformanceEvaluator evaluator,
int maxInstances) |
LearnModel(Classifier learner,
InstanceStream stream,
int maxInstances,
int numPasses) |
LearnModelMultiLabel(Classifier learner,
InstanceStream stream,
int maxInstances,
int numPasses) |
LearnModelMultiTarget(Classifier learner,
InstanceStream stream,
int maxInstances,
int numPasses) |
LearnModelRegression(Classifier learner,
InstanceStream stream,
int maxInstances,
int numPasses) |
Modifier and Type | Field and Description |
---|---|
protected Classifier |
MOA.m_ActualClassifier
the actual moa classifier to use for learning.
|
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