An introduction to variable and feature selection
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
A novel feature selection method to improve classification of gene expression data
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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 for Microarray Data by a LDA-Based Genetic Algorithm
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
An effective gene selection method based on relevance analysis and discernibility matrix
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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
Informative gene selection and tumor classification by null space LDA for microarray data
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Guided rule discovery in XCS for high-dimensional classification problems
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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This paper introduces a new combined filter-wrapper gene subset selection approach where a Genetic Algorithm (GA) is combined with Linear Discriminant Analysis (LDA). This LDA-based GA algorithm has the major characteristic that the GA uses not only a LDA classifier in its fitness function, but also LDA's discriminant coefficients in its dedicated crossover and mutation operators. This paper studies the effect of these informed operators on the evolutionary process. The proposed algorithm is assessed on a several well-known datasets from the literature and compared with recent state of art algorithms. The results obtained show that our filter-wrapper approach obtains globally high classification accuracies with very small number of genes to those obtained by other methods.