Fuzzy rule extraction from ID3-type decision trees for real data

Author

Pal, Nikhil R. ; Chakraborty, Sukumar

Author_Institution

Electron. & Commun. Sci. Unit, Indian Stat. Inst., Calcutta, India

Volume

31

Issue

5

fYear

2001

fDate

10/1/2001 12:00:00 AM

Firstpage

745

Lastpage

754

Abstract

This paper proposes a method to construct a fuzzy rule-based classifier system from an ID3-type decision tree (DT) for real data. The three major steps are rule extraction, gradient descent tuning of the rule-base, and performance-based pruning of the rule-base. Pruning removes all rules which cannot meet a certain level of performance. To test our scheme, we have used the DT generated by RIB3, an ID3-type classifier for real data. In this process, we made some improvements of RID3 to get a tree with less redundancy and hence a smaller rule-base. The rule-base is tested on several data sets and is found to demonstrate an excellent performance. Results obtained by the proposed scheme are consistently better than C4.5 across several data sets

Keywords

decision trees; fuzzy logic; fuzzy systems; knowledge acquisition; knowledge based systems; ID3-type decision trees; data sets; fuzzy rule extraction; fuzzy rule-based classifier system; gradient descent tuning; performance-based pruning; real data; rule extraction; Classification tree analysis; Clustering methods; Data analysis; Data mining; Decision trees; Error correction; Fuzzy systems; Redundancy; Testing; Training data;

fLanguage

English

Journal_Title

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on

Publisher

ieee

ISSN

1083-4419

Type

jour

DOI

10.1109/3477.956036

Filename

956036