keel.Algorithms.Semi_Supervised_Learning.SMOSSL

Class SMOSSLGenerator

java.lang.Object

keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

keel.Algorithms.Semi_Supervised_Learning.SMOSSL.SMOSSLGenerator

public class SMOSSLGenerator
extends PrototypeGenerator

This class implements the Self-traning wrapper. You can use: Knn, C4.5, SMO and Ripper as classifiers.

Author:

triguero

Field Summary

Fields

Modifier and Type	Field and Description
protected int	numberOfClass Number of classes.
protected int	numberOfPrototypes Number of prototypes.

Fields inherited from class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

algorithmName, generatedDataSet, Instancetest, Instancetrain, SEED, seedDefaultValueList, testDataSet, trainingDataSet, transductiveDataSet

Constructor Summary

Constructors

Constructor and Description
SMOSSLGenerator(PrototypeSet _trainingDataSet, int neigbors, int poblacion, int perc, int iteraciones, double c1, double c2, double vmax, double wstart, double wend) Build a new SMOSSLGenerator Algorithm
SMOSSLGenerator(PrototypeSet t, PrototypeSet unlabeled, PrototypeSet test, Parameters parameters) Build a new SMOSSLGenerator Algorithm

Method Summary

Modifier and Type	Method and Description
Pair<PrototypeSet,PrototypeSet>	applyAlgorithm() Apply the SMOSSLGenerator method.
int[]	applySMO(PrototypeSet labeled, PrototypeSet unlabeled) Builds a SMO model with the labeled dataset and classifies the unlabeled dataset.
static void	main(java.lang.String[] args) General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator.

Methods inherited from class keel.Algorithms.Semi_Supervised_Learning.Basic.PrototypeGenerator

absoluteAccuracy, absoluteAccuracyAndError, absoluteAccuracyKNN, accuracy, accuracy2, desordenar_vector_sin, desordenar_vector, execute, generateReducedDataSet, getResultingAccuracy, getResultsOfAccuracy, getSeed, getSetSizeFromPercentage, getSetSizeFromPercentage, getTime, inic_vector_sin, inic_vector, saveResultsOfAccuracyIn, selecRandomSet, setInstanceTest, setInstanceTrain, setSeed

Methods inherited from class java.lang.Object

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

Field Detail

numberOfPrototypes

protected int numberOfPrototypes

Number of prototypes.

numberOfClass

protected int numberOfClass

Number of classes.

Constructor Detail

SMOSSLGenerator

public SMOSSLGenerator(PrototypeSet _trainingDataSet,
                       int neigbors,
                       int poblacion,
                       int perc,
                       int iteraciones,
                       double c1,
                       double c2,
                       double vmax,
                       double wstart,
                       double wend)

Build a new SMOSSLGenerator Algorithm

Parameters:

_trainingDataSet - Original prototype set to be reduced.

neigbors - number of neighbours considered. (not used)

poblacion - population size. (not used)

perc - Reduction percentage of the prototype set.

iteraciones - number of iterations. (not used)

wend - ending w value. (not used)

c1 - class 1 value. (not used)

vmax - maximum v value. (not used)

c2 - class 2 value. (not used)

wstart - starting w value. (not used)

SMOSSLGenerator

public SMOSSLGenerator(PrototypeSet t,
                       PrototypeSet unlabeled,
                       PrototypeSet test,
                       Parameters parameters)

Build a new SMOSSLGenerator Algorithm

Parameters:

t - Original prototype set to be reduced.

unlabeled - Original unlabeled prototype set for SSL.

test - Origital test prototype set.

parameters - Parameters of the algorithm (only % of reduced set).

Method Detail

applySMO

public int[] applySMO(PrototypeSet labeled,
                      PrototypeSet unlabeled)
               throws java.lang.Exception

Builds a SMO model with the labeled dataset and classifies the unlabeled dataset.

Parameters:

labeled - labaled dataset used to build the model.

unlabeled - unlabeled dataset to be classified.

Returns:

the predicted classes for the unlabeled dataset.

Throws:

java.lang.Exception - if the model can not be built.

applyAlgorithm

public Pair<PrototypeSet,PrototypeSet> applyAlgorithm()
                                               throws java.lang.Exception

Apply the SMOSSLGenerator method.

Overrides:

applyAlgorithm in class PrototypeGenerator

Returns:

transductive test sets.

Throws:

java.lang.Exception - if the algorithm can not be applied.

main

public static void main(java.lang.String[] args)

General main for all the prototoype generators Arguments: 0: Filename with the training data set to be condensed. 1: Filename which contains the test data set. 3: Seed of the random number generator. Always. **************************

Parameters:

args - Arguments of the main function.