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An enhancement for heuristic attribute reduction algorithm in rough set

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  • NETL
  • NSTL
  • Elsevier
Attribute reduction is one of the most important issues in the research of rough set theory. Numerous significance measure based heuristic attribute reduction algorithms have been presented to achieve the optimal reduct. However, how to handle the situation that multiple attributes have equally largest significances is still largely unknown. In this regard, an enhancement for heuristic attribute reduction (EHAR) in rough set is proposed. In some rounds of the process of adding attributes, those that have the same largest significance are not randomly selected, but build attribute combinations and compare their significances. Then the most significant combination rather than a randomly selected single attribute is added into the reduct. With the application of EHAR, two representative heuristic attribute reduction algorithms are improved. Several experiments are used to illustrate the proposed EHAR. The experimental results show that the enhanced algorithms with EHAR have a superior performance in achieving the optimal reduct.

Rough setHeuristicAttribute reductionEnhancement for heuristic attributereduction

Kai Zheng、Jie Hu、Zhenfei Zhan、Jin Ma、Jin Qi

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Institute of Knowledge Based Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China

Department of Automobile Engineering, College of Mechanical Engineering, Chongqing University, 174 Shazheng Street, Chongqing 400044, PR China

2014

10.1016/j.eswa.2014.04.042

Expert systems with applications

Expert systems with applications

EISCI
ISSN：0957-4174
年,卷(期)：2014.41(15)
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