title: Cancer Classification in Microarray Data using a Hybrid Selective Independent Component Analysis and υ-Support Vector Machine Algorithm.
abstract: Microarray data have an important role in identification and classification of the cancer tissues. Having a few samples of microarrays in 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 microarray data. An appropriate gene selection method can significantly improve the performance of cancer classification. In this paper, we use selective 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 in making error in order to reconstruct new sample. Then, some of the modified support vector machine (υ-SVM) algorithm sub-classifiers are trained, simultaneously. Eventually, the best sub-classifier with the highest recognition rate is selected. The proposed algorithm is applied on three cancer datasets (leukemia, breast cancer and lung cancer datasets), and its results are compared with other existing methods. The results illustrate that the proposed algorithm (SICA + υ-SVM) has higher accuracy and validity in order to increase the classification accuracy. Such that, our proposed algorithm exhibits relative improvements of 3.3% in correctness rate over ICA + SVM and SVM algorithms in lung cancer dataset.
authors.affiliation: Department of Electrical Engineering, Genomic Signal Processing Laboratory, Sahand University of Technology, Tabriz, Iran.
authors.forename: Hamidreza
authors.initials: H
authors.lastname: Saberkari
authors.affiliation: Department of Electrical Engineering, Genomic Signal Processing Laboratory, Sahand University of Technology, Tabriz, Iran.
authors.forename: Mousa
authors.initials: M
authors.lastname: Shamsi
authors.affiliation: Department of Electrical Engineering, Genomic Signal Processing Laboratory, Sahand University of Technology, Tabriz, Iran.
authors.forename: Mahsa
authors.initials: M
authors.lastname: Joroughi
authors.affiliation: Department of Electrical Engineering, Genomic Signal Processing Laboratory, Sahand University of Technology, Tabriz, Iran.
authors.forename: Faegheh
authors.initials: F
authors.lastname: Golabi
authors.affiliation: Department of Electrical Engineering, Genomic Signal Processing Laboratory, Sahand University of Technology, Tabriz, Iran.
authors.forename: Mohammad Hossein
authors.initials: MH
authors.lastname: Sedaaghi
keywords: Classification; deoxyribonucleic acid; gene selection; independent component analysis; microarray; support vector machine