Feature Selection Package - Algorithms- BLogReg

Description

This algorithm is an implementation of a Bayesian logistic regression method
based on Shevade & Keerthi's algorithm. blogreg.c is the MEX implementaiton written
by Dr. Gavin C. Cawley (paper below), and fsblogreg.m is the MATLAB wrapper for that
code.

Usage

Method Signature:

Output:

*out: * The selected features of the algorithm, represented
as an array of numbers.

Input:

*X: * The list of data points, each row being an instance.

*Y: * The list of data points, each column being a class.

*param: * A struct with the field 'tol', that contains the value
you wish to use for the tolerance.

[*out*] = fsblogreg(*X*, *Y*, *param*)

Output:

Input:

Code Example

% Using the BASEHOCK.m test data set that is

% included in the package.

param.tol = 1;

fsblogreg(X,Y,param)

% included in the package.

param.tol = 1;

fsblogreg(X,Y,param)

Keyword in Evaluator Framework

blogreg

Paper

BibTex entry for:

Gene selection in cancer classification using sparse logistic regression with Bayesian regularisation

Gene selection in cancer classification using sparse logistic regression with Bayesian regularisation

@article{Cawley06,

author = {Cawley, Gavin C. and Talbot, Nicola L. C.},

title = {Gene selection in cancer classification using sparse logistic regression with Bayesian regularization},

journal = {Bioinformatics},

volume={22},

number={19},

year={2006},

issn={1367-4803},

pages={2348--2355}

}

author = {Cawley, Gavin C. and Talbot, Nicola L. C.},

title = {Gene selection in cancer classification using sparse logistic regression with Bayesian regularization},

journal = {Bioinformatics},

volume={22},

number={19},

year={2006},

issn={1367-4803},

pages={2348--2355}

}