Incorporate Cost Matrix into Learning Vector Quantization Modeling: a Comparative Study of Genetic Algorithm, Simulated Annealing and Particle Swarm Optimization



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.


International Journal of Computer Theory and Engineering, vol. 3, no. 1, pp. 122-129, 2011, February 2011

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