Fuzzy rule extraction from ID3-type decision trees for real data.
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.
Pal
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Electron. & Commun. Sci. Unit, Indian Stat. Inst., Calcutta.
Chakraborty
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