Attribute selection with fuzzy decision reducts

被引:197
作者
Cornelis, Chris [1 ]
Jensen, Richard [2 ]
Hurtado, German [1 ,3 ]
Slezak, Dominik [4 ,5 ]
机构
[1] Univ Ghent, Dept Appl Math & Comp Sci, B-9000 Ghent, Belgium
[2] Aberystwyth Univ, Dept Comp Sci, Aberystwyth, Dyfed, Wales
[3] Univ Coll Ghent, Dept Appl Engn Sci, Ghent, Belgium
[4] Univ Warsaw, Inst Math, Warsaw, Poland
[5] Infobright Inc, Warsaw, Poland
基金
比利时弗兰德研究基金会;
关键词
Rough sets; Fuzzy sets; Attribute selection; Data analysis; Decision reducts; ROUGH; SETS; ALGORITHMS; MODEL;
D O I
10.1016/j.ins.2009.09.008
中图分类号
TP [自动化技术、计算机技术];
学科分类号
080201 [机械制造及其自动化];
摘要
Rough set theory provides a methodology for data analysis based on the approximation of concepts in information systems. It revolves around the notion of discernibility: the ability to distinguish between objects, based on their attribute values. It allows to infer data dependencies that are useful in the fields of feature selection and decision model construction. In many cases, however, it is more natural, and more effective, to consider a gradual notion of discernibility. Therefore, within the context of fuzzy rough set theory, we present a generalization of the classical rough set framework for data-based attribute selection and reduction using fuzzy tolerance relations. The paper unifies existing work in this direction, and introduces the concept of fuzzy decision reducts, dependent on an increasing attribute subset measure. Experimental results demonstrate the potential of fuzzy decision reducts to discover shorter attribute subsets, leading to decision models with a better coverage and with comparable, or even higher accuracy. (C) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码:209 / 224
页数:16
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