Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/4242
Title: A fuzzy record-to-record travel algorithm for solving rough set attribute reduction
Authors: Mafarja, Majdi
Abdullah, Salwani
Keywords: System theory;Rough sets;Artificial intelligence;Fuzzy logic
Issue Date: 2013
Abstract: Attribute reduction can be defined as the process of determining a minimal subset of attributes from an original set of attributes. This paper proposes a new attribute reduction method that is based on a record-to-record travel algorithm for solving rough set attribute reduction problems. This algorithm has a solitary parameter called the DEVIATION, which plays a pivotal role in controlling the acceptance of the worse solutions, after it becomes pre-tuned. In this paper, we focus on a fuzzy-based record-to-record travel algorithm for attribute reduction (FuzzyRRTAR). This algorithm employs an intelligent fuzzy logic controller mechanism to control the value of DEVIATION, which is dynamically changed throughout the search process. The proposed method was tested on standard benchmark data sets. The results show that FuzzyRRTAR is efficient in solving attribute reduction problems when compared with other meta-heuristic approaches
URI: http://hdl.handle.net/20.500.11889/4242
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