Tabu search for attribute reduction in rough set theory

Attribute reduction of an information system is a key problem in rough set theory and its applications. Using computational intelligence (CI) tools to solve such problems has recently fascinated many researchers. CI tools are practical and robust for many real-world problems, and they are rapidly developed nowadays. However, some classes of CI tools, like memory-based heuristics, have not been involved in solving information systems and data mining applications like other well-known CI tools of evolutionary computing and neural networks. In this paper, we consider a memory-based heuristic of tabu search to solve the attribute reduction problem in rough set theory. The proposed method, called tabu search attribute reduction (TSAR), shows promising and competitive performance compared with some other CI tools in terms of solution qualities. Moreover, TSAR shows a superior performance in saving the computational costs.