Simultaneous Optimization of Feature Weighting and Instance Selection in Case-based Reasoning Systems Using Genetic Algorithms

Case-based reasoning (CBR) often shows significant promise for improving effectiveness of complex and unstructured decision making. Consequently, it has been applied to various problem-solving areas including manufacturing, finance and marketing. However, the design of appropriate case retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most of previous studies to improve the effectiveness for CBR have focused on the selection of appropriate instances and the optimization of case features and their weights. However, these approaches have been applied independently. Here 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 new model to bankruptcy prediction. Experimental results show that simultaneously optimized CBR outperforms other CBR techniques.