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

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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).

Original languageEnglish (US)
Pages (from-to)361-374
Number of pages14
JournalOmega
Volume29
Issue number4
DOIs
StatePublished - Aug 1 2001

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management

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