On Bayesian case matching

Case retrieval is an important problem in several commercially significant application areas, such as industrial configuration and manufacturing problems. In this paper we extend the Bayesian probability theory based approaches to case-based reasoning, focusing on the case matching task, an essential part of any case retrieval system. Traditional approaches to the case matching problem typically rely on some distance measure, e.g., the Euclidean or Hamming distance, although there is no a priori guarantee that such measures really reflect the useful similarities and dissimilarities between the cases. One of the main advantages of the Bayesian framework for solving this problem is that it forces one to explicitly recognize all the assumptions made about the problem domain, which helps in analyzing the performance of the resulting system. As an example of an implementation of the Bayesian case matching approach in practice, we demonstrate how to construct a case retrieval system based on a set of independence assumptions between the domain variables. In the experimental part of the paper, the Bayesian case matching metric is evaluated empirically in a case-retrieval task by using public domain discrete real-world databases. The results suggest that case retrieval systems based on the Bayesian case matching score perform much better than case retrieval systems based on the standard Hamming distance similarity metrics.