Attribute selection with fuzzy decision reducts
Attribute selection with fuzzy decision reducts
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DOI：
10.1016/j.ins.2009.09.008
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发表时间：
2010-01-15
影响因子：
8.1
通讯作者：
Slezak, Dominik
中科院分区：
文献类型：
--
作者：
Cornelis, Chris;Jensen, Richard;Slezak, Dominik
粗糙集理论提供了一种基于信息系统中概念近似的数据分析方法。它围绕着可辨性的概念：根据属性值区分对象的能力。它允许推断在特征选择和决策模型构建领域有用的数据依赖关系。然而，在许多情况下，更自然、更有效的做法是考虑渐进式的可分辨性概念。因此，在模糊粗糙集理论的背景下，我们提出了基于数据的属性选择和模糊容限关系约简的经典粗糙集框架的推广。在此基础上，结合已有的研究成果，引入了依赖于属性子集测度的模糊决策约简概念。实验结果表明，模糊决策约简可以发现更短的属性子集，从而产生具有更好覆盖范围和相当甚至更高精度的决策模型。(C) 2009爱思唯尔公司版权所有。
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.