org.encog.ml.importance

Class NeuralFeatureImportanceCalc

java.lang.Object

org.encog.ml.importance.AbstractFeatureImportance

org.encog.ml.importance.NeuralFeatureImportanceCalc

All Implemented Interfaces:

FeatureImportance

public class NeuralFeatureImportanceCalc
extends AbstractFeatureImportance

A feature ranking algorithm based on the weights of a neural network. This algorithm can only be used for neural networks, and it cannot calculate importance relative to a new dataset. Sources: Garson, D. G. (1991). Interpreting neural network connection weights. Goh, A. (1995). Back-propagation neural networks for modeling complex systems. Artificial Intelligence in Engineering, 9(3), 143-151.

Constructor Summary

Constructors

Constructor and Description
NeuralFeatureImportanceCalc()

Method Summary

Modifier and Type	Method and Description
void	performRanking() Perform the ranking, without using a specific training set.
void	performRanking(MLDataSet theDataset) Perform the ranking, using a specific training set.

Methods inherited from class org.encog.ml.importance.AbstractFeatureImportance

getFeatures, getFeaturesSorted, getModel, init, toString

Methods inherited from class java.lang.Object

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

Constructor Detail

NeuralFeatureImportanceCalc

public NeuralFeatureImportanceCalc()

Method Detail

performRanking

public void performRanking()

Perform the ranking, without using a specific training set. Not all ranking algorithms support this.

performRanking

public void performRanking(MLDataSet theDataset)

Perform the ranking, using a specific training set. Not all ranking algorithms can make use of a dataset.

Parameters:

theDataset - The dataset.