Hybrid genetic algorithm and fuzzy clustering for bankruptcy prediction

Display Omitted A survey of the related studies according to statistical, intelligent and hybrid approaches was given.A new hybrid soft computing for bankruptcy prediction was proposed in which novel fitness function designs are presented for the GA based financial ratio selection.A public database applied in many studies was adopted in the experiments.Comparison between the proposed financial selection method with other approaches was given.Comparison between the proposed classifier with the well-applied artificial NN approach was given. In the design of a financial bankruptcy prediction model, financial ratio selection and classifier design play major roles. Methodology based on expert opinion, statistical theory and computational intelligence technique has been widely applied. In this study, a hybrid structure integrating statistical theory and computational intelligence technique was developed using genetic algorithm (GA) with statistical measurements and fuzzy logic based fitness functions for key ratio selection. A fuzzy clustering algorithm was used for the classifier design. In the experiments, two financial ratio sets, one extracted from the suggestions of other studies and the other obtained by using the GA toolbox in the SAS statistical software package, were applied to examine the proposed ratio selection schemes. For classifier design, the developed fuzzy classifier was compared with the well known BPNN classifier frequently used in other studies. Besides, comparison between the developed hybrid structure and other well applied structures was also given. Experimental results based on one to four years of financial data prior to the occurrence of bankruptcy were used to evaluate the performance of the proposed prediction model.

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