A probabilistic examplar based model

Abstract

A central problem in case based reasoning (CBR) is how to store and retrievecases. One approach to this problem is to use exemplar based models, where onlythe prototypical cases are stored. However, the development of an exemplar basedmodel (EBM) requires the solution of several problems: (i) how can a EBM berepresented? (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 thesis develops a new model, called a probabilistic exemplar based model,that addresses these research questions. The model utilizes Bayesian networksto develop a suitable representation and uses probability theory to develop thefoundations of the developed model. A probability propagation method is usedto retrieve exemplars when a new case is presented and for assessing the prototypicalityof an exemplar.The model learns incrementally by revising the exemplars retained and byupdating the conditional probabilities required by the Bayesian network. Theproblem of ignorance, encountered when only a few cases have been observed,is tackled by introducing the concept of a virtual exemplar to represent all theunseen cases.The model is implemented in C and evaluated on three datasets. It is alsocontrasted with related work in CBR and machine learning (ML)