Modifier and Type | Interface and Description |
---|---|
interface |
MultiLabelClassifier |
interface |
MultiTargetLearnerSemiSupervised |
interface |
MultiTargetRegressor
MultiTargetRegressor interface for incremental MultiTarget regression models.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMultiLabelLearner |
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 | 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 | 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 | Class and Description |
---|---|
class |
MultiTargetNoChange
MultiTargetNoChange class regressor.
|
Modifier and Type | Class and Description |
---|---|
class |
AMRulesRegressor |
Modifier and Type | Class and Description |
---|---|
class |
RandomAMRules
Random AMRules algoritgm that performs analogous procedure as the Random Forest Trees but with Rules
|
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 | Field and Description |
---|---|
protected MultiLabelLearner |
LearningLiteral.learner |
Modifier and Type | Method and Description |
---|---|
void |
MultiLabelRule.setLearner(MultiLabelLearner learner) |
void |
LearningLiteral.setLearner(MultiLabelLearner learner) |
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 |
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