Abstract—Cost-sensitive learning is an important topic in bankruptcy prediction concerning the unequal misclassification cost of different classes. Learning vector quantization (LVQ) is a powerful tool to solve bankruptcy prediction problem as a classification task. The heuristic algorithms are applied widely in conjunction with artificial intelligent methods for solving optimization problems. The hybridization of heuristic techniques with existing classification algorithms is well illustrated in the field of bankruptcy prediction. In this paper, three hybrid heuristic-based LVQ approaches which combine LVQ with genetic algorithm, simulated annealing and particle swarm optimization respectively, are proposed to minimize the total misclassified cost under the asymmetric cost preference. The idea behind the hybrid classifier is the adoption of heuristic algorithms for the determination of the connection weights of the LVQ network. Experiments on French private company data show the proposed approaches offer interesting and viable alternatives for predictive reinforcement in cost-sensitive context.
Index Terms—bankruptcy prediction, learning vector quantization, heuristic algorithm, asymmetric misclassification cost, cost-sensitive learning, expected misclassified cost.
Ning Chen is with GECAD, Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto (telphone: 351-22-8340500; fax: 351-22-8321159; email: ningchen74@gmail.com).
Bernardete Ribeiro is with CISUC, Department of Informatics Engineering, University of Coimbra, Portugal (email: bribeiro@dei.uc.pt).
Armando Vieira is with Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto (email: asv@isep.ipp.pt).
Joao Duarte is with GECAD,.Instituto Superior de Engenharia do Porto, Instituto Politecnico do Porto (email: jmmd@isep.ipp.pt).
Joao C. Neves is with ISEG, School of Economics, Technical University of Lisbon, Portugal (email: jcneves@iseg.utl.pt).
Cite: Ning Chen, Bernardete Ribeiro, Armando Vieira, Joao Duarte and Joao C. Neves, "Incorporate Cost Matrix into Learning Vector Quantization Modeling: a Comparative Study of Genetic Algorithm, Simulated Annealing and Particle Swarm Optimization," International Journal of Computer Theory and Engineering vol. 3, no. 1, pp. 122-129, 2011.
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