Advertisement

Optimization of Fuzzy Inference System Field Classifiers Using Genetic Algorithms and Simulated Annealing

  • Pretesh B. Patel
  • Tshilidzi Marwala
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

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.

Keywords

Classification fuzzy inference system interactive voice response optimization genetic algorithm simulated annealing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Nichols, C.: The Move from IVR to Speech – Why This is the Right Time to Make the Move to Speech Applications in Customer-Facing Operations. Intervoice (2006)Google Scholar
  2. 2.
  3. 3.
    Patel, P.B., Marwala, T.: Caller Behaviour Classification Using Computational Intelligence Methods. Int. Journal of Neural Systems 20(1), 87–93 (2010)CrossRefGoogle Scholar
  4. 4.
    Panduro, M.A., Brizuela, C.A., Balderas, L.I., Acosta, D.A.: A comparison of genetic algorithms, particle swarm optimization and the differential evolution method for the design of scannable circular antenna arrays. Progress in Electromagnetics Research B 13, 171–186 (2009)CrossRefGoogle Scholar
  5. 5.
    Jones, K.O.: Comparison of genetic algorithms and particle swarm optimization for fermentation feed profile determination. In: Int. Conf. on Computer Systems and Technologies, pp. IIIB.8-1–IIIB.8-7 (2006)Google Scholar
  6. 6.
    Romeo, F., Sangiovanni-Vincentelli, A.: Probabilistic Hill Climbing Algorithms: Properties and Applications. In: Proceedings of the 1985 Chapel Hill Conference on VLSI, pp. 393–417 (1985)Google Scholar
  7. 7.
    Ethni, S.A., Zahawi, B., Giaouris, D., Acarnley, P.P.: Comparison of Particle Swarm and Simulated Annealing Algorithms for Induction Motor Fault Identification. In: 7th IEEE International Conference on Industrial Informatics, INDIN 2009, pp. 470–474 (2009)Google Scholar
  8. 8.
    Manikas, T.W., Cain, J.T.: Genetic Algorithms vs. Simulated Annealing: A Comparison of Approaches for Solving the Circuit Partitioning Problem. Technical report, University of Pittsburgh (1996)Google Scholar
  9. 9.
    VoiceGenie Technologies Inc.: VoiceGenie 7 Tools User’s Guide. VoiceGenie Technologies Inc. (2005) Google Scholar
  10. 10.
    Siler, W., Buckley, J.J.: Fuzzy Expert Systems and Fuzzy Reasoning. John Wiley & Sons, New Jersey (2004)CrossRefGoogle Scholar
  11. 11.
    Elwakdy, A.M., Elsehely, B.E., Eltokhy, C.M., Elhennawy, D.A.: Speech recognition using a wavelet transform to establish fuzzy inference system through subtractive clustering and neural network (ANFIS). Int. Journal of Circuits, Systems and Signal Processing 2, 264–273 (2008)Google Scholar
  12. 12.
    Yen, J., Wang, L.: Constructing optimal fuzzy models using statistical information criteria. Journal of Intelligent and Fuzzy Systems: Applications in Engineering and Technology 7, 185–201 (1999)Google Scholar
  13. 13.
    Akbulut, S., Hasiloglub, A.S., Pamukcu, S.: Data generation for shear modulus and damping ratio in reinforced sands using adaptive neuro-fuzzy inference system. Soil Dynamics and Earthquake Engineering 24, 805–814 (2004)CrossRefGoogle Scholar
  14. 14.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Heidelberg (1996)zbMATHCrossRefGoogle Scholar
  15. 15.
    Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier systems and genetic algorithm. Artificial Intelligence 40(1-3), 235–282 (1989)CrossRefGoogle Scholar
  16. 16.
    Houck, C.R., Joines, J.A., Kay, M.G.: A genetic algorithm for function optimization: a Matlab implementation. Technical Report, North Carolina State University (1995)Google Scholar
  17. 17.
    van Laarhoven, P., Aarts, E.: Simulated Annealing: Theory and Applications (Mathematics and its applications). Kluwer Academic Publishers, Springer, Heidelberg (1987)zbMATHCrossRefGoogle Scholar
  18. 18.
    Ingber, L.: Adaptive simulated annealing (ASA): Lessons learned. Control and Cybernetics 25(1), 33–54 (1996)zbMATHGoogle Scholar
  19. 19.
    Lahtinen, J., Myllymaki, P., Tirri, H.: Empirical comparison of stochastic algorithms. In: Proc. of the Second Nordic Workshop on Genetic Algorithms and their Applications, pp. 45–59 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Pretesh B. Patel
    • 1
  • Tshilidzi Marwala
    • 1
  1. 1.Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa

Personalised recommendations