Rule extraction based on granulation order in interval-valued fuzzy information system

  • Authors:

  • Yi Cheng;Duoqian Miao

  • Affiliations:

  • Department of Equipment and Engineering, Sichuan College of Architectural Technology, Deyang 618000, China and Department of Computer Science and Technology, Tongji University, Shanghai 201804, Ch ...;Department of Computer Science and Technology, Tongji University, Shanghai 201804, China

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Methods of fuzzy rule extraction based on rough set theory are rarely reported in incomplete interval-valued fuzzy information systems. This paper deals with such systems. Instead of obtaining rules by attribute reduction, which may have a negative effect on inducting good rules, the objective of this paper is to extract rules without computing attribute reducts. The data completeness of missing attribute values is first presented. Two different approximation methods are then defined. Two algorithms based on the two approximation methods, called MRBFA and MRBBA are proposed for rule extraction. The two algorithms are evaluated by a housing database from UCI. The experimental results show that MRBFA and MRBBA achieve better classification performances than the method based on attribute reduction.