dragon.ir.classification

Interface Classifier

All Known Implementing Classes:

AbstractClassifier, NBClassifier, NigamActiveLearning, SemanticNBClassifier

public interface Classifier

Interface of Text Classifier

Copyright: Copyright (c) 2005

Company: IST, Drexel University

Version:

1.0

Author:

Davis Zhou

Method Summary

Modifier and Type	Method and Description
DocClassSet	classify(DocClass testingDocs) This method uses the trained model to classify the testing documents.
DocClassSet	classify(DocClassSet trainingDocSet, DocClass testingDocs) This method trains the classifier with the training document set and then using the trained model to classify the testing documents.
DocClassSet	classify(DocClassSet trainingDocSet, DocClassSet validatingDocSet, DocClass testingDocs)
int	classify(IRDoc doc) Classify one particular document
int	classify(Row doc) Classify one particular document
String	getClassLabel(int index) Gets the label of a given document category
FeatureSelector	getFeatureSelector()
IndexReader	getIndexReader()
int[]	rank() Rank all class labels.
void	saveModel(String modelFile) Save the trained classifier model to a file which can be used to restore the classifier later.
void	setFeatureSelector(FeatureSelector selector)
void	train(DocClassSet trainingDocSet) This method trains the classifier with the training document set.
void	train(DocClassSet trainingDocSet, DocClassSet validatingDocSet) This method trains the classifier with the training document set and validating document set.

Method Detail

getIndexReader

IndexReader getIndexReader()

Returns:

the index reader the classifier iw working on

getFeatureSelector

FeatureSelector getFeatureSelector()

Returns:

the feature selector the current classifier is using

setFeatureSelector

void setFeatureSelector(FeatureSelector selector)

Parameters:

selector - the feature selector for the classifier.

train

void train(DocClassSet trainingDocSet)

This method trains the classifier with the training document set.

Parameters:

trainingDocSet - training document set

train

void train(DocClassSet trainingDocSet,
           DocClassSet validatingDocSet)

This method trains the classifier with the training document set and validating document set.

Parameters:

trainingDocSet - training document set

validatingDocSet - validating document set

classify

DocClassSet classify(DocClass testingDocs)

This method uses the trained model to classify the testing documents. The train method should be called before calling this method.

Parameters:

testingDocs - testing document set

Returns:

classified testing document set

classify

DocClassSet classify(DocClassSet trainingDocSet,
                     DocClass testingDocs)

This method trains the classifier with the training document set and then using the trained model to classify the testing documents.

Parameters:

trainingDocSet - training document set

testingDocs - testing document set

Returns:

classified testing document set

classify

DocClassSet classify(DocClassSet trainingDocSet,
                     DocClassSet validatingDocSet,
                     DocClass testingDocs)

Parameters:

trainingDocSet - the training document set

validatingDocSet - the validation document set, usually for avoiding the overfitting problem

testingDocs - the testing document set

Returns:

classified testing document set

classify

int classify(IRDoc doc)

Classify one particular document

Parameters:

doc - the index of the document is stored in the IRDoc object

Returns:

the index of the category of this document. The index starts from zero.

classify

int classify(Row doc)

Classify one particular document

Parameters:

doc - document represented by a Row object

Returns:

the index of the category of this document. The index starts from zero.

getClassLabel

String getClassLabel(int index)

Gets the label of a given document category

Parameters:

index - the index of the category

Returns:

the label of the category

rank

int[] rank()

Rank all class labels. The classify(Row doc) method should be called before calling this method.

Returns:

the ranking of class labels. The first and last element of the returned array contain the most possible label and the least possible label, respectively.

saveModel

void saveModel(String modelFile)

Save the trained classifier model to a file which can be used to restore the classifier later. If the model is not trained yet, this method does nothing. The classifier restored from this model file later can only execute the method classify(Row doc) to determine the label of a document.

Parameters:

modelFile - output file name