title: Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection.
abstract: We investigate the problems of multiclass cancer classification with gene selection from gene expression data. Two different constructed multiclass classifiers with gene selection are proposed, which are fuzzy support vector machine (FSVM) with gene selection and binary classification tree based on SVM with gene selection. Using F test and recursive feature elimination based on SVM as gene selection methods, binary classification tree based on SVM with F test, binary classification tree based on SVM with recursive feature elimination based on SVM, and FSVM with recursive feature elimination based on SVM are tested in our experiments. To accelerate computation, preselecting the strongest genes is also used. The proposed techniques are applied to analyze breast cancer data, small round blue-cell tumors, and acute leukemia data. Compared to existing multiclass cancer classifiers and binary classification tree based on SVM with F test or binary classification tree based on SVM with recursive feature elimination based on SVM mentioned in this paper, FSVM based on recursive feature elimination based on SVM can find most important genes that affect certain types of cancer with high recognition accuracy.
authors.affiliation: National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering and College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, China.
authors.forename: Yong
authors.initials: Y
authors.lastname: Mao
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
authors.forename: Daoying
authors.initials: D
authors.lastname: Pi
authors.forename: Youxian
authors.initials: Y
authors.lastname: Sun
authors.forename: Stephen T C
authors.initials: ST
authors.lastname: Wong