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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Optimization of Fuzzy Inference System Field Classifiers Using Genetic Algorithms and Simulated Annealing
verfasst von : Pretesh B. Patel, Tshilidzi Marwala
Erschienen in: Engineering Applications of Neural Networks
Verlag: Springer Berlin Heidelberg
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A classification system that would aid businesses in selecting calls for analysis would improve the call recording selection process. This would assist in developing good automated self service applications. This paper details such a classification system for a pay beneficiary application. Fuzzy Inference System (FIS) classifiers were created. These classifiers were optimized using Genetic Algorithm (GA) and Simulated Annealing (SA). GA and SA performance in FIS classifier optimization were compared. Good results were achieved. In regards to computational efficiency, SA outperformed GA. When optimizing the FIS ’Say account’ and ’Say confirmation’ classifiers, GA is the preferred technique. Similarly, SA is the preferred method in FIS ’Say amount’ and ’Select beneficiary’ classifier optimization. GA and SA optimized FIS field classifier outperformed previously developed FIS field classifiers.