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A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data

Huerta, Edmundo Bonilla ; Duval, Béatrice ; Hao, Jin-Kao

Applications of Evolutionary Computing, 2006, Vol.3907, p.34-44 [Periódico revisado por pares]

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Título:
A Hybrid GA/SVM Approach for Gene Selection and Classification of Microarray Data
Autor: Huerta, Edmundo Bonilla ; Duval, Béatrice ; Hao, Jin-Kao
Assuntos: Algorithmics. Computability. Computer arithmetics ; Applied sciences ; Bioinformatics ; Biological and medical sciences ; Classification ; Computer Science ; Computer science; control theory; systems ; Data processing. List processing. Character string processing ; Exact sciences and technology ; Feature selection ; Fuzzy logic ; Genetic algorithms ; Medical sciences ; Memory organisation. Data processing ; Microarray data ; Software ; Support vector machines ; Theoretical computing ; Tumors
É parte de: Applications of Evolutionary Computing, 2006, Vol.3907, p.34-44
Descrição: We propose a Genetic Algorithm (GA) approach combined with Support Vector Machines (SVM) for the classification of high dimensional Microarray data. This approach is associated to a fuzzy logic based pre-filtering technique. The GA is used to evolve gene subsets whose fitness is evaluated by a SVM classifier. Using archive records of ”good” gene subsets, a frequency based technique is introduced to identify the most informative genes. Our approach is assessed on two well-known cancer datasets and shows competitive results with six existing methods.
Títulos relacionados: Lecture Notes in Computer Science
Editor: Berlin, Heidelberg: Springer Berlin Heidelberg
Idioma: Inglês

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