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-

SUBMITTER: Mao Y 

PROVIDER: S-EPMC1184049 | biostudies-literature | 2005 Jun

REPOSITORIES: biostudies-literature