An optimal strategy for extracting probabilistic rules by combining rough sets and genetic algorithm

Hang, Xiaoshu and Dai, Honghua 2003, An optimal strategy for extracting probabilistic rules by combining rough sets and genetic algorithm, in Discovery science : 6th international conference, DS 2003, Sapporo, Japan, October 17-19, 2003 : proceedings, Springer Berlin, Berlin, Germany, pp. 153-165.

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Title An optimal strategy for extracting probabilistic rules by combining rough sets and genetic algorithm
Author(s) Hang, Xiaoshu
Dai, Honghua
Conference name International Conference on Discovery Science (6th : 2003 : Sapporo-shi, Japan)
Conference location Sapporo, Hokkaido, Japan
Conference dates October 17-19 2003
Title of proceedings Discovery science : 6th international conference, DS 2003, Sapporo, Japan, October 17-19, 2003 : proceedings
Editor(s) Grieser, Gunter
Tanaka, Yuzuru
Yamamoto, Akihiro
Publication date 2003
Series Lecture notes in computer science ; 2843.
Start page 153
End page 165
Publisher Springer Berlin
Place of publication Berlin, Germany
Summary This paper proposes an optimal strategy for extracting probabilistic rules from databases. Two inductive learning-based statistic measures and their rough set-based definitions: accuracy and coverage are introduced. The simplicity of a rule emphasized in this paper has previously been ignored in the discovery of probabilistic rules. To avoid the high computational complexity of rough-set approach, some rough-set terminologies rather than the approach itself are applied to represent the probabilistic rules. The genetic algorithm is exploited to find the optimal probabilistic rules that have the highest accuracy and coverage, and shortest length. Some heuristic genetic operators are also utilized in order to make the global searching and evolution of rules more efficiently. Experimental results have revealed that it run more efficiently and generate probabilistic classification rules of the same integrity when compared with traditional classification methods.

ISBN 3540202935
9783540202936
ISSN 1611-3349
0302-9743
Language eng
Field of Research 080105 Expert Systems
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2003 Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005007

Document type: Conference Paper
Collection: School of Information Technology
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