public class RandomUnderSampling extends Metodo
File: RandomUnderSampling.java
The Random Under Sampling algorithm is an undersampling method used to deal with the imbalanced problem that deletes negative instances randomly.clasesTest, clasesTrain, datosTest, datosTrain, distanceEu, entradas, ficheroSalida, ficheroTest, ficheroTraining, ficheroValidation, nEntradas, nominalDistance, nominalTrain, nulosTrain, realTest, realTrain, relation, salida, stdDev, test, training
Constructor and Description |
---|
RandomUnderSampling(java.lang.String ficheroScript)
Constructor of the class.
|
Modifier and Type | Method and Description |
---|---|
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.
|
public RandomUnderSampling(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.
ficheroScript
- Name of the configuration script that indicates the
parameters that are going to be used during the execution of the algorithmpublic 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.
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
leerConfiguracion
in class Metodo
ficheroScript
- Name of the configuration script that indicates the
parameters that are going to be used during the execution of the algorithmprotected void normalizar() throws CheckException
normalizar
in class Metodo
CheckException
- Can not be normalized