Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection
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...
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Online Access: | http://dx.doi.org/10.1155/JBB.2005.160 |
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doaj-8c13fb79f05846999e08447e05ec77e12020-11-24T21:50:57ZengHindawi LimitedJournal of Biomedicine and Biotechnology1110-72431110-72512005-01-012005216017110.1155/JBB.2005.160Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene SelectionYong Mao0Xiaobo Zhou1Daoying Pi2Youxian Sun3Stephen T. C. Wong4National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering and College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaHarvard Center for Neurodegeneration & Repair and Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USANational Laboratory of Industrial Control Technology, Institute of Modern Control Engineering and College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaNational Laboratory of Industrial Control Technology, Institute of Modern Control Engineering and College of Information Science and Engineering, Zhejiang University, Hangzhou 310027, ChinaHarvard Center for Neurodegeneration & Repair and Brigham and Women's Hospital, Harvard Medical School, Harvard University, Boston, MA 02115, USAWe 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.http://dx.doi.org/10.1155/JBB.2005.160 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Yong Mao Xiaobo Zhou Daoying Pi Youxian Sun Stephen T. C. Wong |
spellingShingle |
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 Journal of Biomedicine and Biotechnology |
author_facet |
Yong Mao Xiaobo Zhou Daoying Pi Youxian Sun Stephen T. C. Wong |
author_sort |
Yong Mao |
title |
Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection |
title_short |
Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection |
title_full |
Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection |
title_fullStr |
Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection |
title_full_unstemmed |
Multiclass Cancer Classification by Using Fuzzy Support Vector Machine and Binary Decision Tree With Gene Selection |
title_sort |
multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection |
publisher |
Hindawi Limited |
series |
Journal of Biomedicine and Biotechnology |
issn |
1110-7243 1110-7251 |
publishDate |
2005-01-01 |
description |
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. |
url |
http://dx.doi.org/10.1155/JBB.2005.160 |
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