![Sample our Engineering & Technology journals, sign in here to start your access, latest two full volumes FREE to you for 14 days]({"type":"image" , "src" : "/sda/5933/TOC-Subject-Sample-2021-Engineering-Tech.jpg"})
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.
Additional information
Notes on contributors
Majdi Mafarja
Majdi Mafarja received his B.Sc. in software engineering and M.Sc. in computer information systems from Philadelphia University and The Arab Academy for Banking and Financial Sciences, Jordan, in 2005 and 2007, respectively. He did his Ph.D. in computer science at the National University of Malaysia (UKM) in 2012. Now, he works as an assistant professor at the Department of Computer Science at Birzeit University. His research interests include evolutionary computation, meta-heuristics and data mining.
Salwani Abdullah
Salwani Abdullah obtained her B.Sc. in computer science from Universiti Teknologi Malaysia and her master's degree specialising in computer science from Universiti Kebangsaan Malaysia (UKM). She did her Ph.D. in computer science at University of Nottingham, United Kingdom. Now, she is an associate professor and chairperson of the School of Computer Science, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia. Her research interest falls under artificial intelligence and operation research, particularly in meta-heuristic algorithms in the optimisation area that involves different real-world applications and optimisation problems, such as timetabling, scheduling, space allocation and data mining tasks.