Case-based reasoning (CBR) has been applied to various problem-solving areas for a long time because it is suitable to complex and unstructured problems. However, the design of appropriate case retrieval mechanisms to improve the performance of CBR is still a challenging issue. In this paper, we encode the feature weighting and instance selection within the same genetic algorithm (GA) and suggest simultaneous optimization model of feature weighting and instance selection. This study applies the novel model to corporate bankruptcy prediction. Experimental results show that the proposed model out-performs other CBR models. © Springer-Verlag 2004.
CITATION STYLE
Ahn, H., Kim, K. J., & Han, I. (2004). Hybrid genetic algorithms and case-based reasoning systems. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3314, 922–927. https://doi.org/10.1007/978-3-540-30497-5_142
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