Modifier and Type | Interface and Description |
---|---|
interface |
IncrementalMultiLabelClassifier
Interface for incremental multi-label classifiers.
|
interface |
MultiLabelClassifierThreaded
Interface for multi-label classifiers.
|
interface |
SemisupervisedClassifier
SemisupervisedClassifier.java - An Interface for Multilabel Semisupervised Classifiers.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractMultiLabelClassifier
A Multilabel Classifier.
|
class |
BCC
BCC.java - Bayesian Classifier Chains.
|
class |
BPNN
BPNN.java - Back Propagation Neural Network.
|
class |
BR |
class |
BRq
BRq.java - Random Subspace ('quick') Version.
|
class |
CC
CC.java - The Classifier Chains Method.
|
class |
CCq
The Classifier Chains Method - Random Subspace ('quick') Version.
|
class |
CDN
CDN.java - Conditional Dependency Networks.
|
class |
CDT
CDT.java - Conditional Dependency Trellis.
|
class |
CT
CT - Classifier Trellis.
|
class |
DBPNN
DBPNN.java - Deep Back-Propagation Neural Network.
|
class |
FW
FW.java Four-class pairWise classification.
|
class |
HASEL
HASEL - Partitions labels into subsets based on the dataset defined hierarchy.
|
class |
LabelTransformationClassifier
Abstract label transformation classifiers, all classes that transform the labels
should inherit from this classifier.
|
class |
LC
LC.java - The LC (Label Combination) aka LP (Laber Powerset) Method.
|
class |
MajorityLabelset
MajorityLabelset.java - The most simplest multi-label classifier.
|
class |
Maniac
Maniac - Multi-lAbel classificatioN using AutoenCoders.
|
class |
MCC
MCC.java - CC with Monte Carlo optimisation.
|
class |
MLCBMaD
MLC-BMaD - Multi-Label Classification using Boolean Matrix Decomposition.
|
class |
MULAN
MULAN.java - A wrapper for MULAN classifiers MULAN.
|
class |
PCC
PCC.java - (Bayes Optimal) Probabalistic Classifier Chains.
|
class |
PLST
PLST - Principal Label Space Transformation.
|
class |
PMCC
PMCC.java - Like MCC but creates a population of M chains at training time (from Is candidate chains, using Monte Carlo sampling), and uses this population for inference at test time; If you are looking for a 'more typical' majority-vote ensemble method, use something like EnsembleML or BaggingML with MCC.
|
class |
ProblemTransformationMethod
MultilabelClassifier.java - A Multilabel Classifier.
|
class |
PS
PS.java - The Pruned Sets Method.
|
class |
PSt
PSt.java - Pruned Sets with a a threshold so as to be able to predict sets not seen in the training set.
|
class |
RAkEL
RAkEL.java - Draws M subsets of size k from the set of labels, and trains PS upon each one, then combines label votes from these PS classifiers to get a label-vector prediction.
|
class |
RAkELd
RAkELd - Takes RAndom partition of labELs; like RAkEL but labelsets are disjoint / non-overlapping subsets.
|
class |
RT
RT.java - The 'Ranking + Threshold' classifier.
|
Modifier and Type | Method and Description |
---|---|
static MultiLabelClassifier[] |
ProblemTransformationMethod.makeCopies(MultiLabelClassifier model,
int num)
Creates a given number of deep copies of the given multi-label classifier using serialization.
|
static MultiLabelClassifier[] |
AbstractMultiLabelClassifier.makeCopies(MultiLabelClassifier model,
int num)
Creates a given number of deep copies of the given multi-label classifier using serialization.
|
Modifier and Type | Method and Description |
---|---|
static Result |
Evaluation.cvModel(MultiLabelClassifier h,
weka.core.Instances D,
int numFolds,
String top)
CVModel - Split D into train/test folds, and then train and evaluate on each one.
|
static Result |
Evaluation.cvModel(MultiLabelClassifier h,
weka.core.Instances D,
int numFolds,
String top,
String vop)
CVModel - Split D into train/test folds, and then train and evaluate on each one.
|
static Result |
Evaluation.evaluateModel(MultiLabelClassifier h,
weka.core.Instances D_train,
weka.core.Instances D_test)
EvaluateModel - Build model 'h' on 'D_train', test it on 'D_test'.
|
static Result |
Evaluation.evaluateModel(MultiLabelClassifier h,
weka.core.Instances D_train,
weka.core.Instances D_test,
String top)
EvaluateModel - Build model 'h' on 'D_train', test it on 'D_test', threshold it according to 'top', using default verbosity option.
|
static Result |
Evaluation.evaluateModel(MultiLabelClassifier h,
weka.core.Instances D_train,
weka.core.Instances D_test,
String top,
String vop)
EvaluateModel - Build model 'h' on 'D_train', test it on 'D_test', threshold it according to 'top', verbosity 'vop'.
|
static Result |
Evaluation.evaluateModel(MultiLabelClassifier h,
weka.core.Instances D_test,
String tal,
String vop)
EvaluateModel - Assume 'h' is already built, test it on 'D_test', threshold it according to 'top', verbosity 'vop'.
|
static Result |
Evaluation.evaluateModelM(MultiLabelClassifier h,
weka.core.Instances D_train,
weka.core.Instances D_test,
String top,
String vop) |
static void |
ProblemTransformationMethod.evaluation(MultiLabelClassifier h,
String[] args)
Called by classifier's main() method upon initialisation from the command line.
|
static void |
AbstractMultiLabelClassifier.evaluation(MultiLabelClassifier h,
String[] args)
Called by classifier's main() method upon initialisation from the command line.
|
static MultiLabelClassifier[] |
ProblemTransformationMethod.makeCopies(MultiLabelClassifier model,
int num)
Creates a given number of deep copies of the given multi-label classifier using serialization.
|
static MultiLabelClassifier[] |
AbstractMultiLabelClassifier.makeCopies(MultiLabelClassifier model,
int num)
Creates a given number of deep copies of the given multi-label classifier using serialization.
|
static void |
ProblemTransformationMethod.runClassifier(MultiLabelClassifier h,
String[] args)
Called by classifier's main() method upon initialisation from the command line.
|
static void |
AbstractMultiLabelClassifier.runClassifier(MultiLabelClassifier h,
String[] args)
Called by classifier's main() method upon initialisation from the command line.
|
static void |
Evaluation.runExperiment(MultiLabelClassifier h,
String[] options)
RunExperiment - Build and evaluate a model with command-line options.
|
static Result |
Evaluation.testClassifier(MultiLabelClassifier h,
weka.core.Instances D_test)
TestClassifier - test classifier h on D_test
|
static Result |
Evaluation.testClassifierM(MultiLabelClassifier h,
weka.core.Instances D_test)
Test Classifier but threaded (Multiple)
|
Modifier and Type | Class and Description |
---|---|
class |
BRUpdateable
BRUpdateable.java - Updateable BR.
|
class |
CCUpdateable
CCUpdateable.java - Updateable version of CC.
|
class |
MajorityLabelsetUpdateable
MajorityLabelsetUpdateable.java - Updateable version of MajorityLabelset.
|
class |
PSUpdateable
PSUpdateable.java - Pruned Sets Updateable.
|
class |
RTUpdateable
RTUpdateable.java - Updateable RT.
|
Modifier and Type | Method and Description |
---|---|
static Result |
IncrementalEvaluation.evaluateModel(MultiLabelClassifier h,
weka.core.Instances D)
EvaluateModel - over 20 windows.
|
static Result |
IncrementalEvaluation.evaluateModel(MultiLabelClassifier h,
String[] options)
EvaluateModel - Build and evaluate.
|
static Result |
IncrementalEvaluation.evaluateModelBatchWindow(MultiLabelClassifier h,
weka.core.Instances D,
int numWindows,
double rLabeled,
String Top,
String Vop)
EvaluateModelBatchWindow - Evaluate a multi-label data-stream model over windows.
|
static Result |
IncrementalEvaluation.evaluateModelPrequentialBasic(MultiLabelClassifier h,
weka.core.Instances D,
int windowSize,
double rLabeled,
String Top,
String Vop)
Prequential Evaluation - Accuracy since the start of evaluation.
|
static void |
IncrementalEvaluation.runExperiment(MultiLabelClassifier h,
String[] args)
RunExperiment - Build and evaluate a model with command-line options.
|
Modifier and Type | Class and Description |
---|---|
class |
BaggingMLUpdateable
BaggingMLUpdatable.java - Using the OzaBag scheme (see OzaBag.java from MOA)).
|
Modifier and Type | Class and Description |
---|---|
class |
BaggingML
BaggingML.java - Combining several multi-label classifiers using Bootstrap AGGregatING.
|
class |
BaggingMLdup
BaggingMLdup.java - A version of BaggingML where Instances are duplicated instead of assigned higher weighs.
|
class |
CM
CM.java - Classification Maximization using any multi-label classifier.
|
class |
DeepML
DeepML.java - Deep Multi-label Classification.
|
class |
EM
EM.java - Expectation Maximization using any multi-label classifier.
|
class |
EnsembleML
EnsembleML.java - Combines several multi-label classifiers in a simple-subset ensemble.
|
class |
FilteredClassifier
Allows the application of a filter in conjunction with a multi-label classifier.
|
class |
MBR
MBR.java - Meta BR: BR stacked with feature outputs into another BR.
|
class |
MetaProblemTransformationMethod
MultilabelMetaClassifier.java - For ensembles of multi-label methods.
|
class |
RandomSubspaceML
RandomSubspaceML.java - Subsample the attribute space and instance space randomly for each ensemble member.
|
class |
SubsetMapper
Maps the output of a multi-label classifier to a known label combination using the hamming distance.
|
Modifier and Type | Field and Description |
---|---|
protected MultiLabelClassifier[] |
MetaProblemTransformationMethod.m_Classifiers |
Modifier and Type | Class and Description |
---|---|
class |
ARAMNetwork
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
ARAMNetworkClass
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
ARAMNetworkfast
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
ARAMNetworkSparse
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
ARAMNetworkSparseH
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
ARAMNetworkSparseHT
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
ARAMNetworkSparseHT_Strange
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
ARAMNetworkSparseV
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
HARAMNetwork
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
WARAM
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
class |
WvARAM
****REPLACE THE FOLLOWING WITH SIMILAR INFORMATION.
|
Modifier and Type | Class and Description |
---|---|
class |
AbstractDeepNeuralNet
AbstractDeepNeuralNet.java - Extends AbstractNeuralNet with depth options.
|
class |
AbstractNeuralNet
AbstractNeuralNet.java - Provides common options, constants, and other functions for NNs.
|
Modifier and Type | Class and Description |
---|---|
class |
CCp
CCp.java - Multitarget CC with probabilistic output.
|
class |
CR |
class |
NSR
NSR.java - The Nearest Set Relpacement (NSR) method.
|
class |
SCC
SCC.java - Super Class Classifier (aka Super Node Classifier).
|
Modifier and Type | Class and Description |
---|---|
class |
BaggingMT
BaggingMT.java - The Multi-Target Version of BaggingML.
|
class |
EnsembleMT
The Multi-Target Version of EnsembleML.
|
Modifier and Type | Field and Description |
---|---|
protected MultiLabelClassifier[] |
DefaultExperiment.m_Classifiers
the classifiers to evaluate.
|
Modifier and Type | Method and Description |
---|---|
MultiLabelClassifier[] |
DefaultExperiment.getClassifiers()
Returns the classifiers to be evaluated.
|
MultiLabelClassifier[] |
Experiment.getClassifiers()
Returns the classifiers to be evaluated.
|
Modifier and Type | Method and Description |
---|---|
protected void |
DefaultExperiment.notifyIterationNotificationListeners(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Notifies all listeners of a new classifier/dataset combination.
|
void |
DefaultExperiment.setClassifiers(MultiLabelClassifier[] value)
Sets the classifiers to be evaluated.
|
void |
Experiment.setClassifiers(MultiLabelClassifier[] value)
Sets the classifiers to be evaluated.
|
Modifier and Type | Field and Description |
---|---|
protected MultiLabelClassifier |
EvaluationStatistics.m_Classifier
the classifier.
|
Modifier and Type | Method and Description |
---|---|
MultiLabelClassifier |
EvaluationStatistics.getClassifier()
Returns the classifier for these statistics.
|
Modifier and Type | Method and Description |
---|---|
static List<MultiLabelClassifier> |
EvaluationStatisticsUtils.classifiers(List<EvaluationStatistics> stats,
boolean sort)
Returns all the unique classifiers of all the statistics.
|
Modifier and Type | Method and Description |
---|---|
static List<Number> |
EvaluationStatisticsUtils.measurements(List<EvaluationStatistics> stats,
MultiLabelClassifier classifier,
weka.core.Instances dataset,
String measurement)
Returns all the values of a specific measurement for the specified classifier/dataset combination.
|
boolean |
IncrementalEvaluationStatisticsHandler.requires(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Checks whether the specified combination of classifier and dataset is required for evaluation
or already present from previous evaluation.
|
boolean |
KeyValuePairs.requires(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Checks whether the specified combination of classifier and dataset is required for evaluation
or already present from previous evaluation.
|
List<EvaluationStatistics> |
IncrementalEvaluationStatisticsHandler.retrieve(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Retrieves the statis for the specified combination of classifier and dataset.
|
List<EvaluationStatistics> |
KeyValuePairs.retrieve(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Retrieves the statis for the specified combination of classifier and dataset.
|
Constructor and Description |
---|
EvaluationStatistics(MultiLabelClassifier classifier,
weka.core.Instances dataset,
Result result)
Extracts the statistics from the Result object.
|
EvaluationStatistics(MultiLabelClassifier classifier,
String relation,
Result result)
Extracts the statistics from the Result object.
|
Modifier and Type | Method and Description |
---|---|
List<EvaluationStatistics> |
Evaluator.evaluate(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Returns the evaluation statistics generated for the dataset.
|
List<EvaluationStatistics> |
RepeatedRuns.evaluate(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Returns the evaluation statistics generated for the dataset.
|
List<EvaluationStatistics> |
PercentageSplit.evaluate(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Returns the evaluation statistics generated for the dataset.
|
List<EvaluationStatistics> |
CrossValidation.evaluate(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Returns the evaluation statistics generated for the dataset.
|
protected List<EvaluationStatistics> |
RepeatedRuns.evaluateParallel(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Executes the runs in sequential order.
|
protected List<EvaluationStatistics> |
CrossValidation.evaluateParallel(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Returns the evaluation statistics generated for the dataset (parallel execution).
|
protected List<EvaluationStatistics> |
RepeatedRuns.evaluateSequential(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Executes the runs in sequential order.
|
protected List<EvaluationStatistics> |
CrossValidation.evaluateSequential(MultiLabelClassifier classifier,
weka.core.Instances dataset)
Returns the evaluation statistics generated for the dataset (sequential execution).
|
Modifier and Type | Field and Description |
---|---|
protected MultiLabelClassifier |
IterationNotificationEvent.m_Classifier
the classifier.
|
Modifier and Type | Method and Description |
---|---|
MultiLabelClassifier |
IterationNotificationEvent.getClassifier()
Returns the classifier.
|
Constructor and Description |
---|
IterationNotificationEvent(Experiment source,
MultiLabelClassifier classifier,
weka.core.Instances dataset)
Gets called when the experiment starts on a new evaluation.
|
Modifier and Type | Field and Description |
---|---|
protected MultiLabelClassifier |
ClassifyTab.m_LastNonIncrementalClassifier
the last non-incremental classifier in use.
|
Modifier and Type | Method and Description |
---|---|
MultiLabelClassifier |
ClassifyTab.getClassifier()
Returns the current classifier.
|
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