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An empirical study of design and testing of hybrid evolutionary-neural approach for classification

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  • Pendharkar, Parag C.

Abstract

We propose a hybrid evolutionary-neural approach for binary classification that incorporates a special training data over-fitting minimizing selection procedure for improving the prediction accuracy on holdout sample. Our approach integrates parallel global search capability of genetic algorithms (GAs) and local gradient-descent search of the back-propagation algorithm. Using a set of simulated and real life data sets, we illustrate that the proposed hybrid approach fares well, both in training and holdout samples, when compared to the traditional back-propagation artificial neural network (ANN) and a genetic algorithm-based artificial neural network (GA-ANN).

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  • Pendharkar, Parag C., 2001. "An empirical study of design and testing of hybrid evolutionary-neural approach for classification," Omega, Elsevier, vol. 29(4), pages 361-374, August.
    • Handle: RePEc:eee:jomega:v:29:y:2001:i:4:p:361-374
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    References listed on IDEAS

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    1. Parag Pendharkar & James Rodger, 2000. "Nonlinear programming and genetic search application for production scheduling in coal mines," Annals of Operations Research, Springer, vol. 95(1), pages 251-267, January.
    2. Gary J. Koehler, 1991. "Linear Discriminant Functions Determined by Genetic Search," INFORMS Journal on Computing, INFORMS, vol. 3(4), pages 345-357, November.
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    4. Sexton, Randall S. & Dorsey, Robert E. & Johnson, John D., 1999. "Optimization of neural networks: A comparative analysis of the genetic algorithm and simulated annealing," European Journal of Operational Research, Elsevier, vol. 114(3), pages 589-601, May.
    5. Curry, B. & Morgan, P., 1997. "Neural networks: a need for caution," Omega, Elsevier, vol. 25(1), pages 123-133, February.
    6. Abad, P. L. & Banks, W. J., 1993. "New LP based heuristics for the classification problem," European Journal of Operational Research, Elsevier, vol. 67(1), pages 88-100, May.
    7. Selwyn Piramuthu & Harish Ragavan & Michael J. Shaw, 1998. "Using Feature Construction to Improve the Performance of Neural Networks," Management Science, INFORMS, vol. 44(3), pages 416-430, March.
    8. Hung, Ming S. & Denton, James W., 1993. "Training neural networks with the GRG2 nonlinear optimizer," European Journal of Operational Research, Elsevier, vol. 69(1), pages 83-91, August.
    9. Sexton, Randall S. & Alidaee, Bahram & Dorsey, Robert E. & Johnson, John D., 1998. "Global optimization for artificial neural networks: A tabu search application," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 570-584, April.
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    Cited by:

    1. Baykasoglu, Adil & Ozbakir, Lale, 2007. "MEPAR-miner: Multi-expression programming for classification rule mining," European Journal of Operational Research, Elsevier, vol. 183(2), pages 767-784, December.
    2. Pendharkar, Parag C., 2006. "Scale economies and production function estimation for object-oriented software component and source code documentation size," European Journal of Operational Research, Elsevier, vol. 172(3), pages 1040-1050, August.
    3. Chi, Li-Chiu & Tang, Tseng-Chung, 2007. "Impact of reorganization announcements on distressed-stock returns," Economic Modelling, Elsevier, vol. 24(5), pages 749-767, September.
    4. P Pendharkar, 2009. "Misclassification cost minimizing fitness functions for genetic algorithm-based artificial neural network classifiers," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(8), pages 1123-1134, August.
    5. J. J. Glen, 2004. "Dichotomous categorical variable formation in mathematical programming discriminant analysis models," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(4), pages 575-596, June.

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