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.
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Lee, J.W.T., Wang, X., Wang, J. (2006). Reduction of Attributes in Ordinal Decision Systems. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_60
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DOI: https://doi.org/10.1007/11739685_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
Online ISBN: 978-3-540-33585-6
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