Hybrid approaches to attribute reduction based on indiscernibility and discernibility relation

  • Authors:

  • J. Qian;D. Q. Miao;Z. H. Zhang;W. Li

  • Affiliations:

  • Department of Computer Science and Technology, Tongji University, Caoan Road 4800, 201804 Shanghai, China and College of Computer Engineering, Jiangsu Teachers University of Technology, Zhongwu Ro ...;Department of Computer Science and Technology, Tongji University, Caoan Road 4800, 201804 Shanghai, China and Key Laboratory of Embedded System and Service Computing, Ministry of Education of Chin ...;Department of Computer Science and Technology, Tongji University, Caoan Road 4800, 201804 Shanghai, China and Key Laboratory of Embedded System and Service Computing, Ministry of Education of Chin ...;Department of Computer Science and Technology, Tongji University, Caoan Road 4800, 201804 Shanghai, China and Key Laboratory of Embedded System and Service Computing, Ministry of Education of Chin ...

  • Venue:
  • International Journal of Approximate Reasoning
  • Year:
  • 2011

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Abstract

Attribute reduction is one of the key issues in rough set theory. Many heuristic attribute reduction algorithms such as positive-region reduction, information entropy reduction and discernibility matrix reduction have been proposed. However, these methods are usually computationally time-consuming for large data. Moreover, a single attribute significance measure is not good for more attributes with the same greatest value. To overcome these shortcomings, we first introduce a counting sort algorithm with time complexity O(|C| |U|) for dealing with redundant and inconsistent data in a decision table and computing positive regions and core attributes (|C| and |U| denote the cardinalities of condition attributes and objects set, respectively). Then, hybrid attribute measures are constructed which reflect the significance of an attribute in positive regions and boundary regions. Finally, hybrid approaches to attribute reduction based on indiscernibility and discernibility relation are proposed with time complexity no more than max(O(|C|^2|U/C|),O(|C||U|)), in which |U/C| denotes the cardinality of the equivalence classes set U/C. The experimental results show that these proposed hybrid algorithms are effective and feasible for large data.