DocumentCode
2889190
Title
A New Ripple Down Rules Induction Algorithm and its Lattice Extension
Author
Hu, Wei ; Sheng, Huan-Ye
Author_Institution
Comput. Sci. Dept., Shanghai Jiao Tong Univ.
fYear
2006
fDate
13-16 Aug. 2006
Firstpage
1204
Lastpage
1209
Abstract
Ripple down rules (RDR), rules with hierarchically structured exceptions, are effective knowledge representation schemes used in knowledge acquisition (KA). In this paper, a new ripple down rules induction algorithm LRI is proposed. It is originally developed to cope with continuous-valued attributes. After analyzing the central process in LRI in detail, we extend LRI to deal with attributes having lattice structure. Then, we slightly weaken the lattice definition and widely enlarge LRI´s application range. After that, we demonstrate the application of LRI to induce RDR from standard datasets with various kinds of attributes. The experimental results show that the proposed approach is promising. Finally, we conclude our paper with a summary and outlook on future work
Keywords
knowledge acquisition; knowledge based systems; knowledge representation; LRI; RDR; continuous-valued attributes; knowledge acquisition; knowledge representation scheme; lattice structure; ripple down rules induction algorithm; Computer science; Cybernetics; Humans; Knowledge acquisition; Knowledge based systems; Knowledge representation; Lattices; Machine learning; Machine learning algorithms; Lattice Extension; Ripple down rules;
fLanguage
English
Publisher
ieee
Conference_Titel
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location
Dalian, China
Print_ISBN
1-4244-0061-9
Type
conf
DOI
10.1109/ICMLC.2006.258606
Filename
4028247
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