M.A. Esseghir - New Evolutionary Classifier Based on Genetic Algorithms and Neural Networks: Application to the Bankruptcy Forecasting Problem

arima:1879 - Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, November 19, 2007, Volume 6, april 2007, joint Special Issue ARIMA/SACJ on Advances in end-user data mining techniques - https://doi.org/10.46298/arima.1879
New Evolutionary Classifier Based on Genetic Algorithms and Neural Networks: Application to the Bankruptcy Forecasting ProblemArticle

Authors: M.A. Esseghir 1

  • 1 Department of Computer Science [Tunisie]

Artificial neural networks (ANNs) have been widely applied in data mining as a supervised classification technique. The accuracy of this model is mainly provided by its high tolerance to noisy data as well as its ability to classify patterns on which they have not been trained. Moreover, the performance to ANN based models mainly depends both on the ANN parameters and on the quality of input variables. Whereas, an exhaustive search on either appropriate parameters or predictive inputs is very computationally expansive. In this paper, we propose a new hybrid model based on genetic algorithms and artificial neural networks. Our evolutionary classifier is capable of selecting the best set of predictive variables, then, searching for the best neural network classifier and improving classification and generalization accuracies. The designated model was applied to the problem of bankruptcy forecasting, experiments have shown very promising results for the bankruptcy prediction in terms of predictive accuracy and adaptability.


Volume: Volume 6, april 2007, joint Special Issue ARIMA/SACJ on Advances in end-user data mining techniques
Published on: November 19, 2007
Submitted on: April 30, 2007
Keywords: Bankruptcy prediction, Artificial neural networks, Genetic algorithms,Supervised learning,[INFO] Computer Science [cs],[MATH] Mathematics [math]

Consultation statistics

This page has been seen 248 times.
This article's PDF has been downloaded 798 times.