Application of HLVQ and G-Prop Neural Networks to the Problem of Bankruptcy Prediction

Authors:
Armando Vieira;Pedro A. Castillo;Juan J. Merelo
Affiliations:
ISEP-Dep. Física, Porto, Portugal 4200;Dep. de Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain;Dep. de Arquitectura y Tecnología de Computadores, Universidad de Granada, Spain
Venue:
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Year:
2009

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Abstract

Predicting the failure of a company is a difficult problem traditionally performed by accounting experts using heuristic rules extracted from experience. In this work we apply HLVQ, a new algorithm to train neural networks, to this problem and compared its results with G-Prop, a neural network optimized with evolutionary algorithms. We show that HLVQ is an efficient alternative for the bankruptcy prediction problem.