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Soft Computing

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01.08.2011 | Focus

Simulated annealing for supervised gene selection

verfasst von: Maurizio Filippone, Francesco Masulli, Stefano Rovetta

Erschienen in: Soft Computing | Ausgabe 8/2011

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Abstract

Genomic data, and more generally biomedical data, are often characterized by high dimensionality. An input selection procedure can attain the two objectives of highlighting the relevant variables (genes) and possibly improving classification results. In this paper, we propose a wrapper approach to gene selection in classification of gene expression data using simulated annealing along with supervised classification. The proposed approach can perform global combinatorial searches through the space of all possible input subsets, can handle cases with numerical, categorical or mixed inputs, and is able to find (sub-)optimal subsets of inputs giving low classification errors. The method has been tested on publicly available bioinformatics data sets using support vector machines and on a mixed type data set using classification trees. We also propose some heuristics able to speed up the convergence. The experimental results highlight the ability of the method to select minimal sets of relevant features.

Vorheriger Artikel Clustering of protein expression data: a benchmark of statistical and neural approaches

Nächster Artikel Covariance matrix self-adaptation evolution strategies and other metaheuristic techniques for neural adaptive learning

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Titel: Simulated annealing for supervised gene selection
verfasst von: Maurizio Filippone
Francesco Masulli
Stefano Rovetta
Publikationsdatum: 01.08.2011
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 8/2011
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-010-0597-8

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