Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/18280
Title: A rough set approach to selecting attributes for ordinal prediction
Authors: Lee, JWT
Yeung, DS
Tsang, ECC
Keywords: Approximation theory
Classification
Decision making
Decision trees
Rough set theory
Issue Date: 2003
Publisher: IEEE
Source: 2003 International Conference on Machine Learning and Cybernetics, 2-5 November 2003, v. 3, p. 1574-1577 How to cite?
Abstract: Rough set theory has been successfully applied in selecting attributes to improve the effectiveness in derivation of decision trees/rules for classification. When the classification involves ordinal classes, the rough set reduction process should take into consideration the ordering of the classes. In this paper we propose a new way of evaluating and finding reducts for ordinal classification.
URI: http://hdl.handle.net/10397/18280
ISBN: 0-7803-8131-9
DOI: 10.1109/ICMLC.2003.1259746
Appears in Collections:Conference Paper

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