Feature selection via fuzzy clustering

被引：0
|
作者：
Sun, Hao-Jun [1 ]
Sun, Mei [1 ]
Mei, Zhen [2 ]
机构：
[1] Hebei Univ, Coll Math & Comp Sci, Baoding 071002, Peoples R China
[2] Manifold Data Min, Toronto, ON M6N 2J1, Canada
来源：
PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年
关键词：
feature selection; clustering; classification error rate; Fuzzy C-Means;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper deals with feature selection for classification with wrapper framework. We develop a new algorithm for feature selection, based on a fuzzy clustering technique and an iterative process verifying classification accuracy. By monitoring discrepancy between two cluster systems, one derived with full features of the dataset, the other one with a subset of features, we are able to evaluate representation power of the subset of features with respect to the original feature set. Experimental results confirm efficiency of the proposed algorithm.
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页码：1400 / +
页数：2
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