Case-based reasoning supported by genetic algorithms for corporate bond rating

Abstract

A critical issue in case-based reasoning (CBR) is to retrieve not just a similar past case but a usefully similar case to the problem. For this reason, the integration of domain knowledge into the case indexing and retrieving process is highly recommended in building a CBR system. However, this task is difficult to carry out as such knowledge often cannot be successfully and exhaustively captured and represented. This article utilizes a hybrid approach using genetic algorithms (GAs) to case-based retrieval process in an attempt to increase the overall classification accuracy. We propose a machine learning approach using GAs to find an optimal or near optimal weight vector for the attributes of cases in case indexing and retrieving. We apply this weight vector to the matching and ranking procedure of CBR. This GA–CBR integration reaps the benefits of both systems. The CBR technique provides analogical reasoning structures for experience-rich domains while GAs provide CBR with knowledge through machine learning. The proposed approach is demonstrated by applications to corporate bond rating.