On the use of variable complementarity for feature selection in cancer classification

Authors:
Patrick E. Meyer;Gianluca Bontempi
Affiliations:
Université Libre de Bruxelles, Bruxelles, Belgique;Université Libre de Bruxelles, Bruxelles, Belgique
Venue:
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Year:
2006

Citing 8
Cited 6

Elements of information theory

Elements of information theory
Selection of relevant features and examples in machine learning

Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection

Artificial Intelligence - Special issue on relevance
A Formalism for Relevance and Its Application in Feature Subset Selection

Machine Learning
An introduction to variable and feature selection

The Journal of Machine Learning Research
Efficient Feature Selection via Analysis of Relevance and Redundancy

The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information

The Journal of Machine Learning Research
Relevance measures for subset variable selection in regression problems based on k-additive mutual information

Computational Statistics & Data Analysis

Information-theoretic inference of large transcriptional regulatory networks

EURASIP Journal on Bioinformatics and Systems Biology
A Model-Based Relevance Estimation Approach for Feature Selection in Microarray Datasets

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Biological network inference using redundancy analysis

BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
Conditional likelihood maximisation: a unifying framework for information theoretic feature selection

The Journal of Machine Learning Research
Feature Interaction Maximisation

Pattern Recognition Letters
Control-flow integrity principles, implementations, and applications

ACM Transactions on Information and System Security (TISSEC)

Quantified Score

Hi-index	0.01

Visualization

Abstract

The paper presents an original filter approach for effective feature selection in classification tasks with a very large number of input variables. The approach is based on the use of a new information theoretic selection criterion: the double input symmetrical relevance (DISR). The rationale of the criterion is that a set of variables can return an information on the output class that is higher than the sum of the informations of each variable taken individually. This property will be made explicit by defining the measure of variable complementarity. A feature selection filter based on the DISR criterion is compared in theoretical and experimental terms to recently proposed information theoretic criteria. Experimental results on a set of eleven microarray classification tasks show that the proposed technique is competitive with existing filter selection methods.