A genetic algorithm-based approach to cost-sensitive bankruptcy prediction

Authors:
Ning Chen;Bernardete Ribeiro;Armando S. Vieira;João Duarte;João C. Neves
Affiliations:
GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Rua Silvio Lima-Polo II, Coimbra 3030-790, Portugal;GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal;GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto, Rua Dr. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal;ISEG-School of Economics, Technical University of Lisbon, Rua do Quelhas 6, Lisbon 1200-781, Portugal
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

The prediction of bankruptcy is of significant importance with the present-day increase of bankrupt companies. In the practical applications, the cost of misclassification is worthy of consideration in the modeling in order to make accurate and desirable decisions. An effective prediction system requires the integration of the cost preference into the construction and optimization of prediction models. This paper presents an evolutionary approach for optimizing simultaneously the complexity and the weights of learning vector quantization network under the symmetric cost preference. Experimental evidences on a real-world data set demonstrate the proposed algorithm leads to significant reduction of features without the degradation of prediction capability.