IP algorithms in compact rough classification modeling

Article type: Research Article

Authors: Bakar, Azuraliza Abu | Sulaiman, Md Nasir | Othman, Mohamed | Selamat, Mohd Hasan

Affiliations: Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia

Abstract: The paper presents the Integer Programming (IP) algorithms in mining a compact rough classification model. The algorithm is based on creating a 0–1 integer programming model from a rough discernibility relations of a decision system (DS) to find minimum selection of important attributes which is called reduct in rough set theory. A branch and bound search strategy that performs a non-chronological backtracking is proposed to solve the problem. The experimental result shows that the proposed IP algorithm has significantly reduced the number of rules generated from the obtained reducts with high percentage of classification accuracy. The branch and bound search strategy has shown reduction in certain amount of search.

Keywords: rough set, reduct, integer programming (IP), decision system (DS)

DOI: 10.3233/IDA-2001-5505

Journal: Intelligent Data Analysis, vol. 5, no. 5, pp. 419-429, 2001