Welcome to the InfoSci Platform
SAR: An Algorithm for Selecting a Partition Attribute in Categorical-Valued Information System Using Soft Set Theory

SAR: An Algorithm for Selecting a Partition Attribute in Categorical-Valued Information System Using Soft Set Theory

Rabiei Mamat, Tutut Herawan, Mustafa Mat Deris
Copyright: © 2011 |Volume: 1 |Issue: 4 |Pages: 15
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781613507544|DOI: 10.4018/ijirr.2011100103
Cite Article Cite Article

MLA

Mamat, Rabiei, et al. "SAR: An Algorithm for Selecting a Partition Attribute in Categorical-Valued Information System Using Soft Set Theory." IJIRR vol.1, no.4 2011: pp.38-52. http://doi.org/10.4018/ijirr.2011100103

APA

Mamat, R., Herawan, T., & Deris, M. M. (2011). SAR: An Algorithm for Selecting a Partition Attribute in Categorical-Valued Information System Using Soft Set Theory. International Journal of Information Retrieval Research (IJIRR), 1(4), 38-52. http://doi.org/10.4018/ijirr.2011100103

Chicago

Mamat, Rabiei, Tutut Herawan, and Mustafa Mat Deris. "SAR: An Algorithm for Selecting a Partition Attribute in Categorical-Valued Information System Using Soft Set Theory," International Journal of Information Retrieval Research (IJIRR) 1, no.4: 38-52. http://doi.org/10.4018/ijirr.2011100103

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Soft-set theory proposed by Molodstov is a general mathematic tool for dealing with uncertainty. Recently, several algorithms have been proposed for decision making using soft-set theory. However, these algorithms still concern on Boolean-valued information system. In this paper, Support Attribute Representative (SAR), a soft-set based technique for decision making in categorical-valued information system is proposed. The proposed technique has been tested on three datasets to select the best partitioning attribute. Furthermore, two UCI benchmark datasets are used to elaborate the performance of the proposed technique in term of executing time. On these two datasets, it is shown that SAR outperforms three rough set-based techniques TR, MMR, and MDA up to 95% and 50%, respectively. The results of this research will provide useful information for decision makers to handle categorical datasets.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.