Semi-supervised Rough Cost/Benefit Decisions

Issue title: Fundamentals of Knowledge Technology

Article type: Research Article

Authors: Lingras, Pawan | Chen, Min | Miao, Duoqian

Affiliations: Department of Mathematics & Computing Science, Saint Mary's University, Halifax Nova Scotia, B3H 3C3, Canada. E-mail: pawan@cs.smu.ca | School of Electronics and Information Engineering, Tongji University Shanghai, 201804, P.R. China and Key Laboratory of Embedded System & Service Computing Ministry of Education of China, Tongji University, Shanghai 201804, P.R. China

Abstract: Most of the business decisions are based on cost and benefit considerations. Data mining techniques that make it possible for the businesses to incorporate financial considerations will be moremeaningful to the decisionmakers. Decision theoretic framework has been helpful in providing a better understanding of classification models. This study describes a semi-supervised decision theoretic rough set model. The model is based on an extension of decision theoretic model proposed by Yao. The proposal is used to model financial cost/benefit scenarios for a promotional campaign in a real-world retail store.

Keywords: Rough sets, Rough approximation, Probability, Decision theory, Cost/benefit analysis, k-means clustering algorithm

DOI: 10.3233/FI-2009-128

Journal: Fundamenta Informaticae, vol. 94, no. 2, pp. 233-244, 2009