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

作者：Chen Ning^*; Ribeiro Bernardete; Vieira Armando S; Duarte Joao; Neves Joao C

来源：Expert Systems with Applications, 2011, 38(10): 12939-12945.

DOI：10.1016/j.eswa.2011.04.090

• Neural network
• Learning vector quantization
• Classification
• Cost-sensitive learning
• Feature selection
• Genetic algorithm

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.

• 出版日期2011-9-15