A memetic algorithm for gene selection and molecular classification of cancer

Authors:
Béatrice Duval;Jin-Kao Hao;Jose Crispin Hernandez Hernandez
Affiliations:
LERIA, Université d'Angers, ANGERS, France;LERIA, Université d'Angers, ANGERS, France;Instituto Tecnologico de Apizaco, Apizaco, Mexico
Venue:
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Year:
2009

Citing 14
Cited 1

A training algorithm for optimal margin classifiers

COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
An introduction to variable and feature selection

The Journal of Machine Learning Research
Variable selection using svm based criteria

The Journal of Machine Learning Research
Is cross-validation valid for small-sample microarray classification?

Bioinformatics
Markov blanket-embedded genetic algorithm for gene selection

Pattern Recognition
Classification consistency analysis for bootstrapping gene selection

Neural Computing and Applications
Gene selection using genetic algorithm and support vectors machines

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
Gene selection using hybrid particle swarm optimization and genetic algorithm

Soft Computing - A Fusion of Foundations, Methodologies and Applications
A study of cross-validation and bootstrap for accuracy estimation and model selection

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A genetic embedded approach for gene selection and classification of microarray data

EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Selection for feature gene subset in microarray expression profiles based on a hybrid algorithm using SVM and GA

ISPA'06 Proceedings of the 2006 international conference on Frontiers of High Performance Computing and Networking
A hybrid GA/SVM approach for gene selection and classification of microarray data

EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Bayesian learning with local support vector machines for cancer classification with gene expression data

EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing

Hybridising harmony search with a Markov blanket for gene selection problems

Information Sciences: an International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

Choosing a small subset of genes that enables a good classification of diseases on the basis of microarray data is a difficult optimization problem. This paper presents a memetic algorithm, called MAGS, to deal with gene selection for supervised classification of microarray data. MAGS is based on an embedded approach for attribute selection where a classifier tightly interacts with the selection process. The strength of MAGS relies on the synergy created by combining a problem specific crossover operator and a dedicated local search procedure, both being guided by relevant information from a SVM classifier. Computational experiments on 8 well-known microarray datasets show that our memetic algorithm is very competitive compared with some recently published studies.