Biological Data Classification Using Rough Sets and Support Vector Machines

被引：0
|
作者：
Zhao, Yanjun [1 ]
Zhang, Yanqing [1 ]
Xiong, Naixue [1 ]
机构：
[1] Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA
来源：
2009 ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY | 2009年
关键词：
rough sets; support vector machines; attribute reduction; biological data classification; information entropy; RECOGNITION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Biological data classification is an important data mining research area in biomedical applications. The current challenge problem is that there is a large number of condition attributes (features) in biological data, with which it is difficult for classification methods to deal. In this paper, a new approach based on rough sets and support vector machines is proposed for biological data classification. Rough sets theory is a good mathematical tool to make attribute reduction by removing redundant condition attributes (features). Furthermore, the new rough support vector machines use the new information entropy of rough sets as uncertainty measurement to reflect the whole uncertainty information. Simulation results demonstrate that this new approach is useful in terms of classification accuracy and the number of attributes.
引用
收藏
页码：344 / 349
页数：6
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