DocumentCode
401697
Title
A rough set approach to selecting attributes for ordinal prediction
Author
Lee, John W T ; Yeung, Daniel S. ; Tsang, Eric C C
Author_Institution
Dept. of Comput., Hong Kong Polytech. Univ., China
Volume
3
fYear
2003
fDate
2-5 Nov. 2003
Firstpage
1574
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.
Keywords
approximation theory; classification; decision making; decision trees; rough set theory; attribute selection; decision trees; ordinal classes; ordinal classification; rough set reduction process; rough set theory; Classification tree analysis; Context modeling; Data analysis; Decision making; Decision trees; Electronic mail; Fuzzy set theory; Information analysis; Information systems; Set theory;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2003 International Conference on
Print_ISBN
0-7803-8131-9
Type
conf
DOI
10.1109/ICMLC.2003.1259746
Filename
1259746
Link To Document