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ASME Press Select Proceedings
International Conference on Software Technology and Engineering, 3rd (ICSTE 2011)Available to Purchase
ISBN:
9780791859797
No. of Pages:
760
Publisher:
ASME Press
Publication date:
2011
eBook Chapter
52 A New Heuristic Approach for Training Data Reduction and a Genetic Learning Method for Acheiving Compact Fuzzy Rule—Based Systems Available to Purchase
By
Tri Minh Huynh
Tri Minh Huynh
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Page Count:
11
-
Published:2011
Citation
Huynh, TM. "A New Heuristic Approach for Training Data Reduction and a Genetic Learning Method for Acheiving Compact Fuzzy Rule—Based Systems." International Conference on Software Technology and Engineering, 3rd (ICSTE 2011). Ed. Othman, M, & Kasim, RSR. ASME Press, 2011.
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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.
Topics:
Genetic algorithms
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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