PEBM: A Probabilistic Exemplar Based Model

Abstract

A central problem in case based reasoning (CBR) is how to store and retrieve cases. One approach to this problem is to use exemplar based models, where only the prototypical cases are stored. However, the development of an exemplar based model (EBM) requires the solution of several problems: (i) how can a EBM be represented? (ii) given a new case, how can a suitable exemplar be retrieved? (iii) what makes a good exemplar? (iv) how can an EBM be learned incrementally? This paper develops a new model , called a probabilistic exemplar based model, that addresses these questions. The model utilizes Bayesian networks to develop a suitable representation and uses probabilistic propagation for assessing and retrieving exemplars when a new case is presented. The model learns incrementally by revising the exemplars retained and by updating the conditional probabilities required by the Bayesian network. The paper also pr esents the results of evaluating the model on three datasets.