The cluster-indexing method for case-based reasoning using self-organizing maps and learning vector quantization for bond rating cases

摘要

This paper presents a hybrid data mining model for the prediction of corporate bond rating. This model uses a new case-indexing method of case-based reasoning (CBR), which utilizes the cluster information of financial data in order to improve classification accuracy. This method uses not only case-specific knowledge of past problems like conventional CBR, but also uses additional knowledge derived from the clusters of cases. The cluster-indexing method assumes that there are some distinct subgroups (clusters) in each rated group. Competitive artificial neural networks are used to generate the centroid values of clusters because these techniques produce better adaptive clusters than statistical clustering algorithms. The experiments using corporate bond rating cases show that the cluster-indexing CBR is superior to conventional CBR and inductive learning-indexing CBR—a rival case indexing method.