Global optimization of feature weights and the number of neighbors that combine in a case-based reasoning system

Abstract

Case-based reasoning (CBR) often shows significant promise for improving the 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 indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most previous studies on improving the effectiveness of CBR have focused on the similarity function aspect or optimization of case features and their weights. However, according to some of the prior research, finding the optimal k parameter for the k-nearest neighbor is also crucial for improving the performance of the CBR system. Nonetheless, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence techniques. In this study, we introduce a genetic algorithm to optimize the number of neighbors that combine, as well as the weight of each feature. The new model is applied to the real-world case of a major telecommunication company in Korea in order to build a prediction model for customer profitability level. Experimental results show that our genetic-algorithm-optimized CBR approach outperforms other artificial intelligence techniques for this multi-class classification problem.