Incremental Maintenance of Rough Fuzzy Set Approximations under the Variation of Object Set

Issue title: Advances on Rough Sets and Knowledge Technology

Article type: Research Article

Authors: Zeng, Anping | Li, Tianrui | Zhang, Junbo | Chen, Hongmei

Affiliations: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China. [email protected]; [email protected]; [email protected]; [email protected]

Note: [] Also works: School of Computer and Information Engineering, Yibin University, Yibin 644007, China

Note: [] Address for correspondence: School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

Abstract: The lower and upper approximations in rough set theory will change dynamically over time due to the variation of the information system. Incremental methods for updating approximations in rough set theory and its extensions have received much attention recently. Most existing incremental methods have difficulties in dealing with fuzzy decision systems which decision attributes are fuzzy. This paper introduces an incremental algorithm for updating approximations of rough fuzzy sets under the variation of the object set in fuzzy decision systems. In experiments on 6 data sets from UCI, comparisons of the incremental and non-incremental methods for updating approximations are conducted. The experimental results show that the incremental method effectively reduces the computational time.

Keywords: Rough Fuzzy Sets, Approximations, Incremental Learning, Fuzzy Decision System

DOI: 10.3233/FI-2014-1051

Journal: Fundamenta Informaticae, vol. 132, no. 3, pp. 401-422, 2014