Feature Selection Package - Algorithms - Fisher Score
Description
The fisher score is a method for determining the most relevant features for
classification. It uses discriminative methods, and generative statistical
models to accomplish this.
Usage
Method Signature:
[out] = fsFisher(X,Y)
Output:
out: A struct containing the following fields
- W - The distribution at each data point.
- fList - The list of features that are deemed useful.
- prf - This means that the smaller the feature weight is, the
more useful it will be to the user.
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
fsFisher(X,Y)
Keyword in Evaluator Framework
fisher
Paper
BibTex entry for:
R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. John Wiley & Sons, New York, 2 edition, 2001.
@BOOK{Duda-etal01,
title = {Pattern Classification},
publisher = {John Wiley \& Sons, New York},
year = {2001},
author = {Duda, R.O. and Hart, P.E. and Stork, D.G.},
edition = {2},
}
}