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ASME Press Select Proceedings
International Conference on Software Technology and Engineering, 3rd (ICSTE 2011)Available to Purchase
By
Mohamed Othman
Mohamed Othman
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Raja Suzana Raja Kasim
Raja Suzana Raja Kasim
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ISBN:
9780791859797
No. of Pages:
760
Publisher:
ASME Press
Publication date:
2011

This paper is to introduce a heuristic method for selecting a subset of instances from the training data set in high dimensional problems. This subset is called the representative training data set (RTR). A proposed genetic algorithm (GA) is used to learn a compact fuzzy rule-based system (FRBS) with the instances of RTR. RTR size is rather smaller than the initial training data set, thus time cost for learning FRBS decreases significantly. Therein the number of fuzzy rules is reduced. The smaller size of the rule base is closely related to the interpretability of the FRBS. As a result, the final FBRS gets a suitable and acceptable balance between interpretability and accuracy.

Abstract
Key Words
1 Introduction
2. The Representative Training Data Set (RTR)
3. Generating the Initial KB
4. Tuning the Initial KB with RTR
5. Experimental Study
6. Conclusions
References
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