Modifier and Type | Class and Description |
---|---|
class |
AbstractMOAObject
Abstract MOA Object.
|
Modifier and Type | Method and Description |
---|---|
MOAObject |
AbstractMOAObject.copy() |
MOAObject |
MOAObject.copy()
This method produces a copy of this object.
|
static MOAObject |
AbstractMOAObject.copy(MOAObject obj)
This method produces a copy of an object.
|
Modifier and Type | Method and Description |
---|---|
static MOAObject |
AbstractMOAObject.copy(MOAObject obj)
This method produces a copy of an object.
|
static int |
AbstractMOAObject.measureByteSize(MOAObject obj)
Gets the memory size of an object.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Classifier
Classifier interface for incremental classification models.
|
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 |
---|---|
MOAObject |
AbstractClassifier.getModel() |
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 | Class and Description |
---|---|
class |
FixedBM |
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 |
AttributeSplitSuggestion
Class for computing attribute split suggestions given a split test.
|
Modifier and Type | Interface and Description |
---|---|
interface |
AttributeClassObserver
Interface for observing the class data distribution for an attribute.
|
interface |
DiscreteAttributeClassObserver
Interface for observing the class data distribution for a discrete (nominal) attribute.
|
interface |
NumericAttributeClassObserver
Interface for observing the class data distribution for a numeric attribute.
|
Modifier and Type | Class and Description |
---|---|
class |
BinaryTreeNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute using a binary tree.
|
class |
BinaryTreeNumericAttributeClassObserverRegression
Class for observing the class data distribution for a numeric attribute using a binary tree.
|
class |
FIMTDDNumericAttributeClassObserver |
class |
GaussianNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute using gaussian estimators.
|
class |
GreenwaldKhannaNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute using Greenwald and Khanna methodology.
|
class |
NominalAttributeClassObserver
Class for observing the class data distribution for a nominal attribute.
|
class |
NullAttributeClassObserver
Class for observing the class data distribution for a null attribute.
|
class |
VFMLNumericAttributeClassObserver
Class for observing the class data distribution for a numeric attribute as in VFML.
|
Modifier and Type | Class and Description |
---|---|
class |
InstanceConditionalBinaryTest
Abstract binary conditional test for instances to use to split nodes in Hoeffding trees.
|
class |
InstanceConditionalTest
Abstract conditional test for instances to use to split nodes in Hoeffding trees.
|
class |
NominalAttributeBinaryTest
Nominal binary conditional test for instances to use to split nodes in Hoeffding trees.
|
class |
NominalAttributeMultiwayTest
Nominal multi way conditional test for instances to use to split nodes in Hoeffding trees.
|
class |
NumericAttributeBinaryTest
Numeric binary conditional test for instances to use to split nodes in Hoeffding trees.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ChangeDetector
Change Detector interface to implement methods that detects change.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractChangeDetector
Abstract Change Detector.
|
class |
ADWIN
ADaptive sliding WINdow method.
|
class |
ADWINChangeDetector
Drift detection method based in ADWIN.
|
class |
CusumDM
Drift detection method based in Cusum
|
class |
DDM
Drift detection method based in DDM method of Joao Gama SBIA 2004.
|
class |
EDDM
Drift detection method based in EDDM method of Manuel Baena et al.
|
class |
EnsembleDriftDetectionMethods
Ensemble Drift detection method
|
class |
EWMAChartDM
Drift detection method based in EWMA Charts of Ross, Adams, Tasoulis and Hand
2012
|
class |
GeometricMovingAverageDM
Drift detection method based in Geometric Moving Average Test
|
class |
HDDM_A_Test
Online drift detection method based on Hoeffding's bounds.
|
class |
HDDM_W_Test
Online drift detection method based on McDiarmid's bounds.
|
class |
PageHinkleyDM
Drift detection method based in Page Hinkley Test.
|
class |
RDDM |
class |
SEEDChangeDetector
Drift detection method as published in:
|
class |
SeqDrift1ChangeDetector
SeqDrift1ChangeDetector.java.
|
class |
SeqDrift1ChangeDetector.SeqDrift1
SeqDrift1 uses sliding window to build a sequential change detection model
that uses statistically sound guarantees defined using Bernstein Bound on
false positive and false negative rates.
|
class |
SeqDrift2ChangeDetector
SeqDriftChangeDetector.java.
|
class |
SeqDrift2ChangeDetector.SeqDrift2
SeqDrift2 uses reservoir sampling to build a sequential change detection
model that uses statistically sound guarantees defined using Bernstein Bound
on false positive and false negative rates.
|
class |
STEPD |
Modifier and Type | Interface and Description |
---|---|
interface |
SplitCriterion
Interface for computing splitting criteria.
|
Modifier and Type | Class and Description |
---|---|
class |
GiniSplitCriterion
Class for computing splitting criteria using Gini
with respect to distributions of class values.
|
class |
InfoGainSplitCriterion
Class for computing splitting criteria using information gain
with respect to distributions of class values.
|
class |
InfoGainSplitCriterionMultilabel
Class for computing splitting criteria using information gain with respect to
distributions of class values for Multilabel data.
|
class |
SDRSplitCriterion |
class |
VarianceReductionSplitCriterion |
Modifier and Type | Interface and Description |
---|---|
interface |
StatisticalTest
This interface represents how to perform multivariate statistical tests.
|
Modifier and Type | Class and Description |
---|---|
class |
Cramer
Implements the Multivariate Non-parametric Cramer Von Mises Statistical Test.
|
class |
KNN
Implements the multivariate non-parametric KNN statistical test.
|
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 | 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
|
protected class |
AdaptiveRandomForest.ARFBaseLearner
Inner class that represents a single tree member of the 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 | Class and Description |
---|---|
class |
MajorityLabelset
Majority Labelset classifier.
|
class |
MEKAClassifier
Wrapper for MEKA classifiers.
|
class |
MultilabelHoeffdingTree
Hoeffding Tree for classifying multi-label data.
|
static class |
MultilabelHoeffdingTree.MultilabelInactiveLearningNode |
class |
MultilabelHoeffdingTree.MultilabelLearningNodeClassifier |
Modifier and Type | Class and Description |
---|---|
class |
ICVarianceReduction |
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.
|
static class |
ISOUPTree.InnerNode |
static class |
ISOUPTree.LeafNode |
static class |
ISOUPTree.Node |
static class |
ISOUPTree.SplitNode |
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 |
AbstractAMRules |
class |
AMRulesRegressor |
class |
AMRulesRegressorOld |
class |
BinaryClassifierFromRegressor
Function that convertes a regressor into a binary classifier
baseLearnerOption- regressor learner selection
|
class |
Predicates |
class |
RuleClassification |
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 |
NominalRulePredicate
Class that contains the literal information for a nominal variable
|
class |
NumericRulePredicate
Class that contains the literal information for a numerical variable
|
class |
Rule |
class |
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
|
class |
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
|
class |
RuleSplitNode
A modified SplitNode method implementing the extra information
|
Modifier and Type | Interface and Description |
---|---|
interface |
AnomalyDetector
Anomaly Detector interface to implement methods that detects change.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractAnomalyDetector |
class |
AnomalinessRatioScore
Score for anomaly detection
percentageAnomalousAttributesOption - Percentage of anomalous attributes.
|
class |
NoAnomalyDetection
No anomaly detection is performed
|
class |
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
|
Modifier and Type | Interface and Description |
---|---|
interface |
ProbabilityFunction |
Modifier and Type | Class and Description |
---|---|
class |
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
|
class |
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
|
class |
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
|
Modifier and Type | Class and Description |
---|---|
class |
FIMTDDNumericAttributeClassLimitObserver |
Modifier and Type | Class and Description |
---|---|
class |
NoChangeDetection |
Modifier and Type | Class and Description |
---|---|
class |
NominalAttributeBinaryRulePredicate
Nominal binary conditional test for instances to use to split nodes in rules.
|
class |
NumericAttributeBinaryRulePredicate
Numeric binary conditional test for instances to use to split nodes in
AMRules.
|
Modifier and Type | Interface and Description |
---|---|
interface |
AMRulesSplitCriterion |
Modifier and Type | Class and Description |
---|---|
class |
SDRSplitCriterionAMRules |
class |
SDRSplitCriterionAMRulesNode |
class |
VarianceRatioSplitCriterion |
class |
VRSplitCriterion |
Modifier and Type | Class and Description |
---|---|
class |
AbstractErrorWeightedVote
AbstractErrorWeightedVote class for weighted votes based on estimates of errors.
|
class |
ExpNegErrorWeightedVote
ExpNegErrorWeightedVote class for weighted votes based on estimates of errors.
|
class |
InverseErrorWeightedVote
InverseErrorWeightedVoteMultiLabel class for weighted votes based on estimates of errors.
|
class |
MinErrorWeightedVote
MinErrorWeightedVote class for weighted votes based on estimates of errors.
|
class |
OneMinusErrorWeightedVote |
class |
UniformWeightedVote
UniformWeightedVote class for weighted votes based on estimates of errors.
|
Modifier and Type | Method and Description |
---|---|
MOAObject |
ErrorWeightedVote.copy()
Creates a copy of the object
|
Modifier and Type | Class and Description |
---|---|
class |
ErrorMeasurement
Computes error measures with a fading factor
fadingErrorFactorOption - Fading factor
|
class |
MeanAbsoluteDeviation
Computes the Mean Absolute Deviation for single target regression problems
|
class |
RootMeanSquaredError
Computes the Root Mean Squared Error for single target regression problems
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractFeatureRanking |
class |
BasicFeatureRanking
Basic Feature Ranking method
João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.
|
class |
MeritFeatureRanking
Merit Feature Ranking method
João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.
|
class |
NoFeatureRanking
No feature ranking is performed
|
class |
WeightedMajorityFeatureRanking
Weighted Majority Feature Ranking method
João Duarte, João Gama,Feature ranking in hoeffding algorithms for regression.
|
Modifier and Type | Interface and Description |
---|---|
interface |
AMRulesLearner |
interface |
AMRulesRegressorFunction |
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 | 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 | Interface and Description |
---|---|
interface |
AttributeStatisticsObserver
Interface for observing the statistics for an attribute.
|
interface |
NominalStatisticsObserver |
interface |
NumericStatisticsObserver |
Modifier and Type | Class and Description |
---|---|
class |
MultiLabelBSTree
Binary search tree for AMRules splitting points determination
|
class |
MultiLabelBSTreeFloat |
class |
MultiLabelNominalAttributeObserver
Function for determination of splitting points for nominal variables
|
class |
SingleVector
Vector of float numbers with some utilities.
|
Modifier and Type | Class and Description |
---|---|
class |
AttributeExpansionSuggestion
Class for computing attribute split suggestions given a split test.
|
class |
LearningLiteral |
class |
LearningLiteralClassification
This class contains the functions for learning the literals for Multi-label classification
(in same way as Multi-Target regression).
|
class |
LearningLiteralRegression |
class |
Literal |
class |
MultiLabelRule |
class |
MultiLabelRuleClassification |
class |
MultiLabelRuleRegression |
class |
ObservableMOAObject |
Modifier and Type | Interface and Description |
---|---|
interface |
MultiLabelSplitCriterion |
Modifier and Type | Class and Description |
---|---|
class |
MultilabelInformationGain
Multi-label Information Gain.
|
class |
MultiTargetVarianceRatio |
Modifier and Type | Class and Description |
---|---|
class |
AbstractErrorWeightedVoteMultiLabel
AbstractErrorWeightedVote class for weighted votes based on estimates of errors.
|
class |
FirstHitVoteMultiLabel
FirstHitVoteMultiLabel class for weighted votes based on estimates of errors.
|
class |
InverseErrorWeightedVoteMultiLabel
InverseErrorWeightedVoteMuliLabel class for weighted votes based on estimates of errors.
|
class |
UniformWeightedVoteMultiLabel
UniformWeightedVote class for weighted votes based on estimates of errors.
|
Modifier and Type | Method and Description |
---|---|
MOAObject |
ErrorWeightedVoteMultiLabel.copy()
Creates a copy of the object
|
Modifier and Type | Interface and Description |
---|---|
interface |
MultiLabelErrorMeasurer |
interface |
MultiTargetErrorMeasurer |
Modifier and Type | Class and Description |
---|---|
class |
AbstractMultiLabelErrorMeasurer |
class |
AbstractMultiTargetErrorMeasurer |
class |
MeanAbsoluteDeviationMT
Mean Absolute Deviation for multitarget and with fading factor
|
class |
RelativeMeanAbsoluteDeviationMT
Relative Mean Absolute Deviation for multitarget and with fading factor
|
class |
RelativeRootMeanSquaredErrorMT
Relative Root Mean Squared Error for multitarget and with fading factor
|
class |
RootMeanSquaredErrorMT
Root Mean Squared Error for multitarget and with fading factor
|
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 | Interface and Description |
---|---|
interface |
InputAttributesSelector |
Modifier and Type | Class and Description |
---|---|
class |
MeritThreshold
Input selection algorithm based on Merit threshold
|
class |
SelectAllInputs
Does not selects inputs
|
Modifier and Type | Interface and Description |
---|---|
interface |
InstanceTransformer
Interface for instance transformation
|
Modifier and Type | Class and Description |
---|---|
class |
InstanceAttributesSelector
Transforms instances considering both a subset of input attributes
and a subset of output attributes
|
class |
InstanceOutputAttributesSelector
Transforms instances considering only a subset of output attributes
|
class |
NoInstanceTransformation
Performs no transformation.
|
Modifier and Type | Class and Description |
---|---|
class |
MultiLabelRandomAMRules |
Modifier and Type | Interface and Description |
---|---|
interface |
OutputAttributesSelector |
Modifier and Type | Class and Description |
---|---|
class |
EntropyThreshold
Entropy measure use by online multi-label AMRules for heuristics computation.
|
class |
SelectAllOutputs |
class |
StdDevThreshold |
class |
VarianceThreshold |
Modifier and Type | Interface and Description |
---|---|
interface |
IademNumericAttributeObserver |
Modifier and Type | Class and Description |
---|---|
class |
Iadem2 |
class |
Iadem3 |
class |
Iadem3Subtree |
class |
IademAttributeSplitSuggestion |
class |
IademGaussianNumericAttributeClassObserver |
class |
IademGreenwaldKhannaNumericAttributeClassObserver |
class |
IademGreenwaldKhannaQuantileSummary |
class |
IademNominalAttributeBinaryTest |
class |
IademNominalAttributeMultiwayTest |
class |
IademNumericAttributeBinaryTest |
class |
IademVFMLNumericAttributeClassObserver |
Modifier and Type | Class and Description |
---|---|
class |
CFCluster |
class |
Cluster |
class |
Clustering |
class |
SphereCluster
A simple implementation of the
Cluster interface representing
spherical clusters. |
Modifier and Type | Interface and Description |
---|---|
interface |
Clusterer |
Modifier and Type | Class and Description |
---|---|
class |
AbstractClusterer |
class |
ClusterGenerator |
class |
CobWeb
Class implementing the Cobweb and Classit clustering algorithms.
|
class |
WekaClusteringAlgorithm |
Modifier and Type | Class and Description |
---|---|
class |
Clustream
Citation: CluStream: Charu C.
|
class |
ClustreamKernel |
class |
WithKmeans |
Modifier and Type | Class and Description |
---|---|
class |
ClusKernel
Representation of an Entry in the tree
|
class |
ClusTree
Citation: ClusTree: Philipp Kranen, Ira Assent, Corinna Baldauf, Thomas Seidl:
The ClusTree: indexing micro-clusters for anytime stream mining.
|
Modifier and Type | Class and Description |
---|---|
class |
MicroCluster |
class |
Timestamp |
class |
WithDBSCAN |
Modifier and Type | Class and Description |
---|---|
class |
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.
|
class |
Dstream
Citation: Y.
|
class |
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.
|
Modifier and Type | Class and Description |
---|---|
class |
BICO
A instance of this class provides the BICO clustering algorithm.
|
class |
ClusteringFeature
Provides a ClusteringFeature.
|
class |
ClusteringTreeHeadNode
Provides a ClusteringTreeNode with an extended nearest neighbor search in the
root.
|
class |
ClusteringTreeNode
Provides a tree of ClusterFeatures.
|
Modifier and Type | Class and Description |
---|---|
class |
NonConvexCluster |
Modifier and Type | Class and Description |
---|---|
class |
MyBaseOutlierDetector |
Modifier and Type | Class and Description |
---|---|
class |
AbstractC |
class |
AbstractCBase |
Modifier and Type | Class and Description |
---|---|
class |
ApproxSTORM |
class |
ExactSTORM |
class |
STORMBase |
Modifier and Type | Class and Description |
---|---|
class |
AnyOut |
class |
AnyOutCore |
Modifier and Type | Class and Description |
---|---|
class |
MCOD |
class |
MCODBase |
Modifier and Type | Class and Description |
---|---|
class |
SimpleCOD |
class |
SimpleCODBase |
Modifier and Type | Class and Description |
---|---|
class |
StreamKM |
Modifier and Type | Class and Description |
---|---|
class |
AutoExpandVector<T>
Vector with the capability of automatic expansion.
|
class |
DoubleVector
Vector of double numbers with some utilities.
|
class |
GaussianEstimator
Gaussian incremental estimator that uses incremental method that is more resistant to floating point imprecision.
|
class |
GreenwaldKhannaQuantileSummary
Class for representing summaries of Greenwald and Khanna quantiles.
|
class |
Measurement
Class for storing an evaluation measurement.
|
Modifier and Type | Method and Description |
---|---|
MOAObject |
AutoExpandVector.copy() |
Modifier and Type | Class and Description |
---|---|
class |
Converter
Converter.
|
Modifier and Type | Interface and 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.
|
Modifier and Type | Class and Description |
---|---|
class |
Accuracy |
class |
AdwinClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an adaptive sliding
window.
|
class |
ALMeasureCollection
Collection of measures used to evaluate AL tasks.
|
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 |
ChangeDetectionMeasures |
class |
CMM |
class |
EntropyCollection |
class |
EWMAClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using an Exponential Weighted Moving Average.
|
class |
F1 |
class |
FadingFactorClassificationPerformanceEvaluator
Classification evaluator that updates evaluation results using a fading factor.
|
class |
General |
class |
LearningEvaluation
Class that stores an array of evaluation measurements.
|
class |
MeasureCollection |
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 |
OutlierPerformance |
class |
RegressionAccuracy |
class |
Separation |
class |
SilhouetteCoefficient |
class |
SSQ |
class |
StatisticalCollection |
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.
|
Modifier and Type | Class and Description |
---|---|
class |
LearningCurve
Class that stores and keeps the history of evaluation measurements.
|
class |
Preview
Abstract class which is used to define the methods needed from a preview
|
class |
PreviewCollection<CollectionElementType extends Preview>
Class that stores and keeps the history of multiple previews
|
class |
PreviewCollectionLearningCurveWrapper
Class used to wrap LearningCurve so that it can be used in
conjunction with a PreviewCollection
|
Modifier and Type | Interface and Description |
---|---|
interface |
Learner<E extends Example>
Learner interface for incremental learning models.
|
interface |
LearnerSemiSupervised<E extends Example> |
Modifier and Type | Class and Description |
---|---|
class |
ChangeDetectorLearner
Class for detecting concept drift and to be used as a learner.
|
Modifier and Type | Method and Description |
---|---|
MOAObject |
Learner.getModel()
Gets the model if this learner.
|
Modifier and Type | Interface and Description |
---|---|
interface |
OptionHandler
Interface representing an object that handles options or parameters.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractOptionHandler
Abstract Option Handler.
|
Modifier and Type | Class and Description |
---|---|
class |
MemRecommenderData |
Modifier and Type | Class and Description |
---|---|
class |
FlixsterDataset |
class |
JesterDataset |
class |
MovielensDataset |
Modifier and Type | Class and Description |
---|---|
class |
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.
|
class |
BRISMFPredictor
Implementation of the algorithm described in Scalable
Collaborative Filtering Approaches for Large Recommender
Systems (Gábor Takács, István Pilászy, Bottyán Németh,
and Domonkos Tikk).
|
Modifier and Type | Interface and 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.
|
Modifier and Type | Class and Description |
---|---|
class |
ArffFileStream
Stream reader of ARFF files.
|
class |
BootstrappedStream
Bootstrapped Stream
|
class |
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 them
|
class |
RecurrentConceptDriftStream
Stream generator that adds recurrent concept drifts to examples in a stream.
|
Modifier and Type | Class and Description |
---|---|
class |
ClusteringStream |
class |
FileStream |
class |
RandomRBFGeneratorEvents |
class |
SimpleCSVStream
Provides a simple input stream for csv files.
|
Modifier and Type | Interface and Description |
---|---|
interface |
MultiLabelStreamFilter |
interface |
StreamFilter
Interface representing a stream filter.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMultiLabelStreamFilter
Abstract Stream Filter.
|
class |
AbstractStreamFilter
Abstract Stream Filter.
|
class |
AddNoiseFilter
Filter for adding random noise to examples in a stream.
|
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 |
Modifier and Type | Class and 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.
|
Modifier and Type | Interface and Description |
---|---|
interface |
ConceptDriftGenerator |
Modifier and Type | Class and Description |
---|---|
class |
AbruptChangeGenerator |
class |
AbstractConceptDriftGenerator |
class |
GradualChangeGenerator |
class |
NoChangeGenerator |
Modifier and Type | Class and Description |
---|---|
class |
MetaMultilabelGenerator
Stream generator for multilabel data.
|
class |
MultilabelArffFileStream
Stream reader for ARFF files of multilabel data.
|
Modifier and Type | Interface and Description |
---|---|
interface |
Task
Interface representing a task.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractTask
Abstract Task.
|
class |
AuxiliarMainTask
Abstract Auxiliar Main Task.
|
class |
CacheShuffledStream
Task for storing and shuffling examples in memory.
|
class |
ClassificationMainTask
Abstract Classification Main Task.
|
class |
ConceptDriftMainTask |
class |
EvaluateClustering
Task for evaluating a clusterer on a stream.
|
class |
EvaluateConceptDrift
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
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 |
EvaluateModelMultiLabel
Task for evaluating a static model on a stream.
|
class |
EvaluateModelMultiTarget
Task for evaluating a static model on a stream.
|
class |
EvaluateModelRegression
Task for evaluating a static model on a stream.
|
class |
EvaluateMultipleClusterings
Task for evaluating a clusterer on multiple (related) streams.
|
class |
EvaluateOnlineRecommender
Test for evaluating a recommender by training and periodically testing
on samples from a rating dataset.
|
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 |
EvaluatePrequentialMultiLabel
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
class |
EvaluatePrequentialMultiTarget
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
class |
EvaluatePrequentialMultiTargetSemiSuper
Multi-target Prequential semi-supervised evaluation
Phase1: Creates a initial model with
|
class |
EvaluatePrequentialRegression
Task for evaluating a classifier on a stream by testing then training with each example in sequence.
|
class |
FailedTaskReport
Class for reporting a failed task.
|
class |
LearnModel
Task for learning a model without any evaluation.
|
class |
LearnModelMultiLabel
Task for learning a model without any evaluation.
|
class |
LearnModelMultiTarget
Task for learning a model without any evaluation.
|
class |
LearnModelRegression
Task for learning a model without any evaluation.
|
class |
MainTask
Abstract Main Task.
|
class |
MeasureStreamSpeed
Task for measuring the speed of the stream.
|
class |
MultiLabelMainTask |
class |
MultiTargetMainTask |
class |
Plot
A task allowing to create and plot gnuplot scripts.
|
class |
RegressionMainTask
Abstract Regression Main Task.
|
class |
RunStreamTasks
Task for running several experiments modifying values of parameters.
|
class |
RunTasks
Task for running several experiments modifying values of parameters.
|
class |
WriteMultipleStreamsToARFF
Task to output multiple streams to a ARFF files using different random seeds
|
class |
WriteStreamToARFFFile
Task to output a stream to an ARFF file
|
Modifier and Type | Class and Description |
---|---|
class |
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.
|
class |
ALMultiParamTask
This task individually evaluates an active learning classifier for each
element of a set of parameter values.
|
class |
ALPartitionEvaluationTask
This task extensively evaluates an active learning classifier on a stream.
|
class |
ALPrequentialEvaluationTask
This task performs prequential evaluation for an active learning classifier
(testing, then training with each example in sequence).
|
class |
MetaMainTask
This class provides features for handling tasks in a tree-like
structure of parents and subtasks.
|
Modifier and Type | Method and Description |
---|---|
static MOAObject |
MOAUtils.fromCommandLine(ClassOption option,
String commandline)
Turns a commandline into an object (classname + optional options).
|
static MOAObject |
MOAUtils.fromCommandLine(Class requiredType,
String commandline)
Turns a commandline into an object (classname + optional options).
|
static MOAObject |
MOAUtils.fromOption(ClassOption option)
Creates a MOA object from the specified class option.
|
Modifier and Type | Method and Description |
---|---|
static String |
MOAUtils.toCommandLine(MOAObject obj)
Returs the commandline for the given object.
|
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