Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection

Yong Mao, Xiaobo Zhou, Daoying Pi, Youxian Sun, Stephen T C Wong

Research output: Contribution to journalArticle

56 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)160-171
Number of pages12
JournalJournal of Biomedicine and Biotechnology
Volume2005
Issue number2
DOIs
StatePublished - Jun 30 2005

ASJC Scopus subject areas

  • Biotechnology
  • Molecular Medicine
  • Molecular Biology
  • Genetics
  • Health, Toxicology and Mutagenesis

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