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.
引用
收藏
页码：1400 / +
页数：2
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