mulan.classifier.meta.thresholding
Class ExampleBasedFMeasureOptimizer

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

All Implemented Interfaces:

Serializable, MultiLabelLearner, TechnicalInformationHandler

public class ExampleBasedFMeasureOptimizer
extends MultiLabelMetaLearner

This class takes the marginal probabilities estimated for each label by a multi-label learner and transforms them into a bipartition which is approximately optimal for example-based FMeasure. This optimizer assumes independence of the target variables (labels) and the optimal solution always contains the labels with the highest marginal probabilities. For more information, see

David Lewis: Evaluating and optimizing autonomous text classification systems. In: Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 1995), 1995.

BibTeX:

 @inproceedings{Lewis1995,
    author = {David Lewis},
    booktitle = {Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 1995)},
    title = {Evaluating and optimizing autonomous text classification systems},
    year = {1995}
 }
 

Version:

2012.07.16

Author:

Eleftherios Spyromitros-Xioufis

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

ExampleBasedFMeasureOptimizer()
Default constructor

ExampleBasedFMeasureOptimizer(MultiLabelLearner baseLearner)
The supplied multi-label learner should be able to output marginal probabilities.

Method Summary

protected void	buildInternal(MultiLabelInstances trainingSet) 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) Learner specific implementation for predicting on specified data based on trained model.

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

ExampleBasedFMeasureOptimizer

public ExampleBasedFMeasureOptimizer(MultiLabelLearner baseLearner)

The supplied multi-label learner should be able to output marginal probabilities.

Parameters:

baseLearner -

ExampleBasedFMeasureOptimizer

public ExampleBasedFMeasureOptimizer()

Default constructor

Method Detail

buildInternal

protected void buildInternal(MultiLabelInstances trainingSet)
                      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:

trainingSet - the training data set

Throws:

Exception - if learner model was not created successfully

makePredictionInternal

protected MultiLabelOutput makePredictionInternal(Instance instance)
                                           throws Exception,
                                                  InvalidDataException

Description copied from class: MultiLabelLearnerBase

Learner specific implementation for predicting on specified data based on trained model. This method is called from MultiLabelLearnerBase.makePrediction(weka.core.Instance) which guards for model initialization and apply common handling/behavior.

Specified by:

makePredictionInternal in class MultiLabelLearnerBase

Parameters:

instance - the data instance to predict on

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