Reduction of attributes in ordinal decision systems

Authors:
John W. T. Lee;Xizhao Wang;Jinfeng Wang
Affiliations:
Department of computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University, Baoding, China;Machine Learning Center, Faculty of Mathematics and Computer Science, Hebei University, Baoding, China
Venue:
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Year:
2005

Citing 5
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Abstract

Rough set theory has proven to be a very useful tool in dealing with many decision situations where imprecise and inconsistent information are involved. Recently, there are attempts to extent the use of rough set theory to ordinal decision making in which decisions are made on ordering of objects through assigning them to ordinal categories. Attribute reduction is one of the problems that is studied under such ordinal decision situations. In this paper we examine some of the proposed approaches to find ordinal reducts and present a new perspective and approach to the problem based on ordinal consistency.