Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ‑Support Vector Machine Algorithm

Microarray data have an important role in identification and classification of the cancer tissues. having a few samples of microarraysin cancer researches is always one of the most concerns which lead to some problems in designing the classifiers. for this matter,preprocessing gene selection techniques should be utilized before classification to remove the noninformative genes from the microarraydata. an appropriate gene selection method can significantly improve the performance of cancer classification. in this paper, we useselective independent component analysis (sica) for decreasing the dimension of microarray data. using this selective algorithm,we can solve the instability problem occurred in the case of employing conventional independent component analysis (ica) methods.first, the reconstruction error and selective set are analyzed as independent components of each gene, which have a small part inmaking error in order to reconstruct new sample. then, some of the modified support vector machine algorithm sub classifiersare trained, simultaneously. eventually, the best sub classifier with the highest recognition rate is selected. the proposed algorithm isapplied on three cancer datasets (leukemia, breast cancer and lung cancer datasets), and its results are compared with other existingmethods. the results illustrate that the proposed algorithm has higher accuracy and validity in order to increase theclassification accuracy.