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Consistency-preserving attribute reduction in fuzzy rough set framework

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Abstract

Attribute reduction (feature selection) has become an important challenge in areas of pattern recognition, machine learning, data mining and knowledge discovery. Based on attribute reduction, one can extract fuzzy decision rules from a fuzzy decision table. As consistency is one of several criteria for evaluating the decision performance of a decision-rule set, in this paper, we devote to present a consistency-preserving attribute reduction in fuzzy rough set framework. Through constructing the membership function of an object, we first introduce a consistency measure to assess the consistencies of a fuzzy target set and a fuzzy decision table, which underlies a foundation for attribute reduction algorithm. Then, we derive two attribute significance measures based on the proposed consistency measure and design a forward greedy algorithm (ARBC) for attribute reduction from both numerical and nominal data sets. Numerical experiments show the validity of the proposed algorithm from search strategy and heuristic function in the meaning of consistency. Number of the selected features is the least for a given threshold of consistency measure.

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Acknowledgments

This work was supported by the national natural science foundation of China (No. 60903110, 60773133, 70971080), the national high technology research and development program of China (No. 2007AA01Z165), the natural science foundation of Shanxi province, China (No. 2008011038, 2009021017-1).

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Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, Shanxi, China

    Yuhua Qian, Jiye Liang & Wei Wei

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  1. Yuhua Qian
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  2. Jiye Liang
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  3. Wei Wei
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Correspondence to Yuhua Qian.

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Qian, Y., Liang, J. & Wei, W. Consistency-preserving attribute reduction in fuzzy rough set framework. Int. J. Mach. Learn. & Cyber. 4, 287–299 (2013). https://doi.org/10.1007/s13042-012-0090-z

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