Variable selection using probability density function similarity for support vector machine classification of high-dimensional microarray data

摘要

One problem with discriminant analysis of microarray data is representation of each sample by a largenumber of genes that are possibly irrelevant, insignificant or redundant. Methods of variable selection are,therefore, of great significance in microarray data analysis. To circumvent the problem, a new gene miningapproach is proposed based on the similarity between probability density functions on each gene for theclass of interest with respect to the others. This method allows the ascertainment of significant genesthat are informative for discriminating each individual class rather than maximizing the separability ofall classes. Then one can select genes containing important information about the particular subtypes ofdiseases. Based on the mined significant genes for individual classes, a support vector machine with localkernel transform is constructed for the classification of different diseases. The combination of the genemining approach with support vector machine is demonstrated for cancer classification using two publicdata sets. The results reveal that significant genes are identified for each cancer, and the classificationmodel shows satisfactory performance in training and prediction for both data sets.