An evolutionary approach for gene selection and classification of microarray data based on SVM error-bound theories.
Microarrays have thousands to tens-of-thousands of gene features, but only a few hundred patient samples are available. The fundamental problem in microarray data analysis is identifying genes whose disruption causes congenital or acquired disease in humans. In this paper, we propose a new evolutionary method that can efficiently select a subset of potentially informative genes for support vector machine (SVM) classifiers. The proposed evolutionary method uses SVM with a given subset of gene features to evaluate the fitness function, and new subsets of features are selected based on the estimates of generalization error of SVMs and frequency of occurrence of the features in the evolutionary approach. Thus, in theory, selected genes reflect to some extent the generalization performance of SVM classifiers. We compare our proposed method with several existing methods and find that the proposed method can obtain better classification accuracy with a smaller number of selected genes than the existing methods.
Debnath
Rameswar
R
Neuroscience Research Institute, AIST, 1-1-1 Umezono, Tsukuba, Ibaraki, Japan. ramesward@gmail.com
Kurita
Takio
T
D000465:Algorithms; D005075:Biological Evolution; D006801:Humans; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis; D012641:Selection, Genetic