The nature of statistical learning theory
The nature of statistical learning theory
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A review of feature selection techniques in bioinformatics
Bioinformatics
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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
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Feature selection is a widely recognized challenging task in dealing with application problems with a large number of features and a limited number of training samples. Filters and wrappers are the most popular feature selection strategies, but recent literature shows the emergence of hybrid approaches aiming at combining the strengths of filters and wrappers while avoiding their drawbacks. This paper proposes a new hybrid model for feature selection that takes advantage of a filter method to weight the relevance of each feature. Topranked features are selected, in an incremental way, resulting in a set of nested feature spaces of relatively small size. An evolutionary wrapper further refines each space by extracting small subsets of highly predictive features. Extensive experiments on a benchmark microarray dataset state the effectiveness of the proposed approach.