BioMed Research International

BioMed Research International / 2005 / Article
Special Issue

Data Mining in Genomics and Proteomics

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Research article | Open Access

Volume 2005 |Article ID 247093 | https://doi.org/10.1155/JBB.2005.160

Yong Mao, Xiaobo Zhou, Daoying Pi, Youxian Sun, Stephen T. C. Wong, "Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection", BioMed Research International, vol. 2005, Article ID 247093, 12 pages, 2005. https://doi.org/10.1155/JBB.2005.160

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

Received03 Jun 2004
Revised02 Nov 2004
Accepted04 Nov 2004

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.

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.


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