[1]曹晋,张莉,李凡长.一种基于支持向量数据描述的特征选择算法[J].智能系统学报,2015,10(02):215-220.[doi:10.3969/j.issn.1673-4785.201405063]
 CAO Jin,ZHANG Li,LI Fanzhang.A noval support vector data description-based feature selection method[J].CAAI Transactions on Intelligent Systems,2015,10(02):215-220.[doi:10.3969/j.issn.1673-4785.201405063]
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一种基于支持向量数据描述的特征选择算法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第10卷
期数:
2015年02期
页码:
215-220
栏目:
出版日期:
2015-04-25

文章信息/Info

Title:
A noval support vector data description-based feature selection method
作者:
曹晋12 张莉12 李凡长12
1. 苏州大学 计算机科学与技术学院, 江苏 苏州 215006;
2. 苏州大学 计算机信息处理技术省重点实验室, 江苏 苏州 215006
Author(s):
CAO Jin12 ZHANG Li12 LI Fanzhang12
1. Department of Computer Science and Technology, Soochow University, Suzhou 215006, China;
2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou 215006, China
关键词:
支持向量数据描述特征选择递归计算递归特征消除癌症识别基因表达
Keywords:
support vector data descriptionfeature selectionrecursive computationrecursive feature eliminationcancer recognitiongene expression
分类号:
TP391
DOI:
10.3969/j.issn.1673-4785.201405063
文献标志码:
A
摘要:
已有基于支持向量数据描述的特征选择方法计算量较大,导致特征选择的时间过长。针对此问题,提出了一种新的基于支持向量数据描述的特征选择算法。新方法的特征选择是通过超球体球心方向上的能量大小来决定且采用了递归特征消除方式来逐渐剔除掉冗余特征。在Leukemia数据集上的实验结果表明,新方法能够进行快速的特征选择,且所选择的特征对后续的分类是有效的。
Abstract:
There have been proposed feature selection methods based on support vector data description (SVDD), or SVDD-radius-RFE and SVDD-dual-objective-RFE. These methods are time consuming due to the high computational complexity. To remedy it, a support vector data description-based feature selection method is proposed, ie SVDD-RFE. In this method, feature elimination depends on the energy of directions in the center of hypersphere. In addition, a scheme of recursive feature elimination (RFE) is introduced to iteratively remove irrelevant features. Experimental results on the Leukemia dataset showed that this method has fast speed for feature selection, and the selected features are efficient for subsequent classification tasks.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2014-6-4;改回日期:。
基金项目:国家自然科学基金资助项目(61373093,61033013);江苏省自然科学基金资助项目(BK2011284,BK201222725,BK20140008);江苏省高校自然科学研究基金资助项目(13KJA520001).
作者简介:曹晋,女,1991年生,硕士研究生,主要研究方向为模式识别与人工智能;张莉,女,1975年生,教授,博士,主要研究方向为机器学习与模式识别。发表学术论文70篇,合著著作3部,主持国家和省自然科学基金项目5项;李凡长,男,1964年生,教授,博士生导师,主要研究方向为人工智能、机器学习等。先后承担国家自然科学基金重点、面上及省级项目8项,获省级科技奖2项,发表学术论文150余篇,出版专著7部。
通讯作者:曹晋.E-mail:20134527007@stu.suda.edu.cn.
更新日期/Last Update: 2015-06-15