Feature Selection Package - Algorithms - Information Gain
Description
This is a wrapper for the Weka class that computes the information gain
on a class.
Usage
Method Signature:
[out] = fsInfoGain(X,Y)
Output:
out:
A struct containing the following fields:
- w - a list containing the information gain of each feature
when matched with fList.
- fList - the list of features ranked by their ability to classify
the data. fList(1) is the least important feature.
- prf - will always be -1. This means the greater the feature weight,
the more relevant the feature.
Input:
X:
The features on current trunk, each column is a feature vector on all
instances, and each row is a part of the instance.
Y:
The label of instances, in single column form: 1 2 3 4 5 ...
Code Example
% Using the wine.dat data set, which can be found at
% [fspackage_location]/classifiers/knn/wine.mat
fsInfoGain(X,Y)
Keyword in Evaluator Framework
infogain
Paper
BibTex entry for:
Cover, T. M. & Thomas, J. A. Elements of Information Theory Wiley, 1991
@BOOK{Cove-Thom91,
title = {Elements of Information Theory},
publisher = {Wiley},
year = {1991},
author = {Cover, T. M. and Thomas, J. A.}
}