emolib.classifier.machinelearning
Class RiskWeightedNaiveBayes

java.lang.Object
  emolib.util.proc.TextDataProcessor
      emolib.classifier.Classifier
          emolib.classifier.machinelearning.RiskWeightedNaiveBayes

All Implemented Interfaces:

Configurable, DataProcessor

public class RiskWeightedNaiveBayes
extends Classifier

The RiskWeightedNaiveBayes is a Naive Bayes classifier that accounts for the hierarchy of emotion.

It enhances a simple Naive Bayes classifier by weighting the risk of a wrong decision with the distance to the centroid of the predicted sentiment. This classifier is inspired in the Minimum-Error-Rate Classification (Duda, et al., 2001).

--
(Duda, et al., 2001) Duda, R.O., Hart, P.E. and Stork, D.G., "Pattern Classification", New York: John Wiley & Sons, 2001, ISBN: 0-471-05669-3

Author:

Alexandre Trilla (atrilla@salle.url.edu)

See Also:

NaiveBayes

Field Summary

static java.lang.String	PROP_EXTERNAL_FILE Property to indicate a pre-trained classifier.
static java.lang.String	PROP_NB_EXTERNAL_FILE Property to indicate a pre-trained Naive Bayes classifier.
static java.lang.String	PROP_NUM_EMO_DIMS Property to determine the number of emotional dimensions the NaiveBayes deals with.

Constructor Summary

RiskWeightedNaiveBayes()
Main constructor of this classifier.

Method Summary

java.lang.String	getCategory(FeatureBox inputFeatures) The function that decides the most appropriate emotional category.
float	getRisk(FeatureBox feat, java.util.ArrayList<java.lang.Float> losses) Function to retrieve the risk associated to deciding on a class 'c'.
void	initialize() Method to initialize the Classifier.
void	load(java.lang.String path) Generic function to load a previously saved classifier.
void	newProperties(PropertySheet ps) This method is called when this configurable component has new data.
void	register(java.lang.String name, Registry registry) Register my properties.
void	resetExamples() Method to reset the classifier and flush the training examples.
void	save(java.lang.String path) Generic method to save the fully fledged classifier into a given file path.
void	setLearningProcedure(java.lang.String lproc) Method to set the learning procedure.
void	setMomentum(float mom) Method to set the momentum.
void	setNumberOfEmotionalDimensions(int numDims) Method to set the number of emotional dimensions.
void	setThreshold(float phi) Method to set the threshold.
void	trainingProcedure() Void method to train required by the Classifier class.

Methods inherited from class emolib.classifier.Classifier

applyClassification, getData, getListOfExampleCategories, getListOfExampleFeatures, inputTrainingExample, train

Methods inherited from class emolib.util.proc.TextDataProcessor

flush, getName, getPredecessor, setPredecessor, toString

Methods inherited from class java.lang.Object

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

Field Detail

PROP_NUM_EMO_DIMS

public static final java.lang.String PROP_NUM_EMO_DIMS

Property to determine the number of emotional dimensions the NaiveBayes deals with.

See Also:

Constant Field Values

PROP_EXTERNAL_FILE

public static final java.lang.String PROP_EXTERNAL_FILE

Property to indicate a pre-trained classifier.

See Also:

Constant Field Values

PROP_NB_EXTERNAL_FILE

public static final java.lang.String PROP_NB_EXTERNAL_FILE

Property to indicate a pre-trained Naive Bayes classifier.

See Also:

Constant Field Values

Constructor Detail

RiskWeightedNaiveBayes

public RiskWeightedNaiveBayes()

Main constructor of this classifier.

Method Detail

register

public void register(java.lang.String name,
                     Registry registry)
              throws PropertyException

Description copied from interface: Configurable

Register my properties. This method is called once early in the time of the component, shortly after the component is constructed. This component should register any configuration properties that it needs to register. If this configurable extends another configurable, super.register should also be called

Specified by:

register in interface Configurable

Overrides:

register in class Classifier

Parameters:

name - the name of the component

registry - the registry for this component

Throws:

PropertyException

newProperties

public void newProperties(PropertySheet ps)
                   throws PropertyException

Description copied from interface: Configurable

This method is called when this configurable component has new data. The component should first validate the data. If it is bad the component should return false. If the data is good, the component should record the the data internally and return true.

Specified by:

newProperties in interface Configurable

Overrides:

newProperties in class Classifier

Parameters:

ps - a property sheet holding the new data

Throws:

PropertyException - if there is a problem with the properties.

initialize

public void initialize()

Method to initialize the Classifier.

Specified by:

initialize in interface DataProcessor

Overrides:

initialize in class Classifier

setNumberOfEmotionalDimensions

public void setNumberOfEmotionalDimensions(int numDims)

Method to set the number of emotional dimensions.

Parameters:

numDims - The number of emotional dimensions.

setMomentum

public void setMomentum(float mom)

Method to set the momentum.

Parameters:

mom - The momentum.

setThreshold

public void setThreshold(float phi)

Method to set the threshold.

Parameters:

phi - The threshold.

setLearningProcedure

public void setLearningProcedure(java.lang.String lproc)

Method to set the learning procedure.

Parameters:

lproc - The learning procedure.

getRisk

public float getRisk(FeatureBox feat,
                     java.util.ArrayList<java.lang.Float> losses)

Function to retrieve the risk associated to deciding on a class 'c'.

Parameters:

feat - The feature vector.

losses - The loss cost vector related to class 'c', i.e., $\lambda^c$.

Returns:

The associated risk.

getCategory

public java.lang.String getCategory(FeatureBox inputFeatures)

Description copied from class: Classifier

The function that decides the most appropriate emotional category. This is required for any classifier. The classifier in question has to previously run any training algorithm in order to provide the required prediction.

Specified by:

getCategory in class Classifier

Parameters:

inputFeatures - The input emotional features.

Returns:

The most appropriate emotional category.

trainingProcedure

public void trainingProcedure()

Void method to train required by the Classifier class.

Specified by:

trainingProcedure in class Classifier

save

public void save(java.lang.String path)

Description copied from class: Classifier

Generic method to save the fully fledged classifier into a given file path. It is recommended to use a plain text file (such as XML) to save the classifier's configuration since it's readable directly.

Specified by:

save in class Classifier

Parameters:

path - The file path to save the classifier.

load

public void load(java.lang.String path)

Description copied from class: Classifier

Generic function to load a previously saved classifier. This function should be consistent with the design followed in the saving procedure.

Specified by:

load in class Classifier

Parameters:

path - The path of the file which contains the previously saved classifier.

resetExamples

public void resetExamples()

Description copied from class: Classifier

Method to reset the classifier and flush the training examples. This method only makes sense if the classifier in question is trainable and already has some training examples.

Overrides:

resetExamples in class Classifier