mulan.classifier.meta.thresholding
Class MLPTO

java.lang.Object
  mulan.classifier.MultiLabelLearnerBase
      mulan.classifier.meta.MultiLabelMetaLearner
          mulan.classifier.meta.thresholding.MLPTO

All Implemented Interfaces:

Serializable, MultiLabelLearner, TechnicalInformationHandler

public class MLPTO
extends MultiLabelMetaLearner

Class that implements the Multi Label Probabilistic Threshold Optimizer (MLTPTO). For more information, see

J.R. Quevedo, O. Luaces, A. Bahamonde (2012). Multilabel classifiers with a probabilistic thresholding strategy. Pattern Recognition. 45(2):876-883.

BibTeX:

 @article{Quevedo2012,
    author = {J.R. Quevedo and O. Luaces and A. Bahamonde},
    journal = {Pattern Recognition},
    number = {2},
    pages = {876-883},
    publisher = {Elsevier},
    title = {Multilabel classifiers with a probabilistic thresholding strategy},
    volume = {45},
    year = {2012},
    ISSN = {0031-3203}
 }
 

Version:

2012.02.02

Author:

D. Toimil, J. R. Quevedo, O. Luaces

See Also:

Serialized Form

Field Summary

Fields inherited from class mulan.classifier.meta.MultiLabelMetaLearner

baseLearner

Fields inherited from class mulan.classifier.MultiLabelLearnerBase

featureIndices, labelIndices, numLabels

Constructor Summary

MLPTO()
Default constructor

MLPTO(MultiLabelLearner baseLearner, ExampleBasedBipartitionMeasureBase EBBM)

Method Summary

protected void	buildInternal(MultiLabelInstances trainingData) Learner specific implementation of building the model from MultiLabelInstances training data set.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
String	globalInfo() Returns a string describing the multi-label learner.
protected MultiLabelOutput	makePredictionInternal(Instance instance) Produces the optimal bipartition output from a probabilistic multi label output for a predefined loss function.

Methods inherited from class mulan.classifier.meta.MultiLabelMetaLearner

getBaseLearner

Methods inherited from class mulan.classifier.MultiLabelLearnerBase

build, debug, getDebug, isModelInitialized, isUpdatable, makeCopy, makePrediction, setDebug

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

MLPTO

public MLPTO()

Default constructor

MLPTO

public MLPTO(MultiLabelLearner baseLearner,
             ExampleBasedBipartitionMeasureBase EBBM)

Parameters:

baseLearner - the underlying multi-label learner

EBBM - the measure function to be optimized. The measure is optimized minimizing the distance to its ideal value (using IdealValue() method). For measures with 1 as ideal value, like F1 or Accuracy, this algorithm searches for the highest value (the nearest to 1). For measures with 0 as ideal value, like Hamming, this algorithm searches for the lowest value (the nearest to 0).

Method Detail

buildInternal

protected void buildInternal(MultiLabelInstances trainingData)
                      throws Exception

Description copied from class: MultiLabelLearnerBase

Learner specific implementation of building the model from MultiLabelInstances training data set. This method is called from MultiLabelLearnerBase.build(MultiLabelInstances) method, where behavior common across all learners is applied.

Specified by:

buildInternal in class MultiLabelLearnerBase

Parameters:

trainingData - the training data set

Throws:

Exception - if learner model was not created successfully

makePredictionInternal

protected MultiLabelOutput makePredictionInternal(Instance instance)
                                           throws Exception

Produces the optimal bipartition output from a probabilistic multi label output for a predefined loss function. This method evaluates the example using the multi label base-learner to get the labels' probability. Then, it calculates the threshold that optimizes the loss (as especified in the constructor's param FLoss). Finally, this threshold is used to generate a bipartite multi label output.

Specified by:

makePredictionInternal in class MultiLabelLearnerBase

Parameters:

instance - Test example.

Returns:

the output of the learner for the given instance

Throws:

Exception - if an error occurs while making the prediction.

InvalidDataException - if specified instance data is invalid and can not be processed by the learner

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Description copied from class: MultiLabelLearnerBase

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Specified by:

getTechnicalInformation in class MultiLabelLearnerBase

Returns:

the technical information about this class

globalInfo

public String globalInfo()

Description copied from class: MultiLabelLearnerBase

Returns a string describing the multi-label learner.

Specified by:

globalInfo in class MultiLabelLearnerBase

Returns:

a description suitable for displaying in a future gui