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Journal of Biomedicine and Biotechnology
Volume 2005 (2005), Issue 2, Pages 160-171
http://dx.doi.org/10.1155/JBB.2005.160
Research article

Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection

1National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering and College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, China
2Harvard Center for Neurodegeneration & Repair and Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston 02115, MA, USA

Received 3 June 2004; Revised 2 November 2004; Accepted 4 November 2004

Copyright © 2005 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.