keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H

Class SMOTE

java.lang.Object

keel.Algorithms.Preprocess.Basic.Metodo

keel.Algorithms.ImbalancedClassification.ImbalancedAlgorithms.GP_COACH_H.SMOTE

public class SMOTE
extends Metodo

File: SMOTE.java

The SMOTE algorithm is an oversampling method used to deal with the imbalanced problem.

Since:

JDK1.5

Version:

0.1

Author:

Written by Salvador Garcia Lopez (University of Granada) 30/03/2006, Modified by Victoria Lopez Morales (University of Granada) 23/07/2010, Modified by Victoria Lopez Morales (University of Granada) 21/09/2010

Field Summary

Fields inherited from class keel.Algorithms.Preprocess.Basic.Metodo

clasesTest, clasesTrain, datosTest, datosTrain, distanceEu, entradas, ficheroSalida, ficheroTest, ficheroTraining, ficheroValidation, nEntradas, nominalDistance, nominalTrain, nulosTrain, realTest, realTrain, relation, salida, stdDev, test, training

Constructor Summary

Constructors

Constructor and Description
SMOTE(java.lang.String ficheroScript) Constructor of the class.

Method Summary

Modifier and Type	Method and Description
static void	escribeSalida(java.lang.String nombreFichero, InstanceSet datos, Attribute[] entradas, Attribute salida, int nEntradas, java.lang.String relation) Writes results for the test file
static int	evaluationKNNClass(int nvec, double[][] conj, double[][] real, int[][] nominal, boolean[][] nulos, int[] clases, double[] ejemplo, double[] ejReal, int[] ejNominal, boolean[] ejNulos, int nClases, boolean distance, int[] vecinos, int clase) Computes the k nearest neighbors of a given item belonging to a fixed class.
void	leerConfiguracion(java.lang.String ficheroScript) Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class
protected void	normalizar() This function builds the data matrix for reference data and normalizes inputs values
void	run() The main method of the class that includes the operations of the algorithm.

Methods inherited from class java.lang.Object

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

Constructor Detail

SMOTE

public SMOTE(java.lang.String ficheroScript)

Constructor of the class. It configures the execution of the algorithm by reading the configuration script that indicates the parameters that are going to be used.

Parameters:

ficheroScript - Name of the configuration script that indicates the parameters that are going to be used during the execution of the algorithm

Method Detail

run

public void run()

The main method of the class that includes the operations of the algorithm. It includes all the operations that the algorithm has and finishes when it writes the output information into files.

evaluationKNNClass

public static int evaluationKNNClass(int nvec,
                                     double[][] conj,
                                     double[][] real,
                                     int[][] nominal,
                                     boolean[][] nulos,
                                     int[] clases,
                                     double[] ejemplo,
                                     double[] ejReal,
                                     int[] ejNominal,
                                     boolean[] ejNulos,
                                     int nClases,
                                     boolean distance,
                                     int[] vecinos,
                                     int clase)

Computes the k nearest neighbors of a given item belonging to a fixed class. With that neighbors a suggested class for the item is returned.

Parameters:

nvec - Number of nearest neighbors that are going to be searched

conj - Matrix with the data of all the items in the dataset

real - Matrix with the data associated to the real attributes of the dataset

nominal - Matrix with the data associated to the nominal attributes of the dataset

nulos - Matrix with the data associated to the missing values of the dataset

clases - Array with the associated class for each item in the dataset

ejemplo - Array with the data of the specific item in the dataset used as a reference in the nearest neighbor search

ejReal - Array with the data of the real attributes of the specific item in the dataset

ejNominal - Array with the data of the nominal attributes of the specific item in the dataset

ejNulos - Array with the data of the missing values of the specific item in the dataset

nClases - Class of the specific item in the dataset

distance - Kind of distance used in the nearest neighbors computation. If true the distance used is the euclidean, if false the HVMD distance is used

vecinos - Array that will have the nearest neighbours id for the current specific item

clase - Class of the neighbours searched for the item

Returns:

the majority class for all the neighbors of the item

leerConfiguracion

public void leerConfiguracion(java.lang.String ficheroScript)

Obtains the parameters used in the execution of the algorithm and stores them in the private variables of the class

Overrides:

leerConfiguracion in class Metodo

Parameters:

ficheroScript - Name of the configuration script that indicates the parameters that are going to be used during the execution of the algorithm

escribeSalida

public static void escribeSalida(java.lang.String nombreFichero,
                                 InstanceSet datos,
                                 Attribute[] entradas,
                                 Attribute salida,
                                 int nEntradas,
                                 java.lang.String relation)

Writes results for the test file

Parameters:

nombreFichero - Name of the output file

datos - INstances to write

entradas - Input attributes characteristics

salida - Output attribute characteristics

nEntradas - Number of input attributes

relation - Name of the data set

normalizar

protected void normalizar()
                   throws CheckException

This function builds the data matrix for reference data and normalizes inputs values

Overrides:

normalizar in class Metodo

Throws:

CheckException - if the dataset can not be normalized.