A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data
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- Hernandez Hernandez J.C., Duval B., Hao JK. (2007) A Genetic Embedded Approach for Gene Selection and Classification of Microarray Data. In: Marchiori E., Moore J.H., Rajapakse J.C. (eds) Evolutionary Computation,Machine Learning and Data Mining in Bioinformatics. EvoBIO 2007. Lecture Notes in Computer Science, vol 4447. Springer, Berlin, Heidelberg
Classification of microarray data requires the selection of subsets of relevant genes in order to achieve good classification performance. This article presents a genetic embedded approach that performs the selection task for a SVM classifier. The main feature of the proposed approach concerns the highly specialized crossover and mutation operators that take into account gene ranking information provided by the SVM classifier. The effectiveness of our approach is assessed using three well-known benchmark data sets from the literature, showing highly competitive results.
KeywordsMicroarray gene expression Feature selection Genetic Algorithms Support vector machines
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