A study and development of Bayesian exemplar based models

Wu, J 2008, A study and development of Bayesian exemplar based models , PhD thesis, University of Salford.

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Abstract

People think and reason about situations using past experience. Often, past experience consists of stereotypes and exemplars that depict common situations. In order for a computer to reason in a similar way, the identification and representation of exemplars is required. This research aims to investigate and develop a new model, called FReBE, that uses Family Resemblance and Bayesian networks for developing an Exemplar based model. In the thesis, the broad area of research is stated and the areas of background work to be studied are identified and reviewed. An exemplar based model based on family resemblance, clustering and Bayesian networks is developed and implemented. An empirical evaluation of the model is carried out using fifteen well-known benchmark datasets, such as Breast Cancer, Monks, and Heart Disease. The data sets selected include those with discrete attributes, numerical attributes and even those with missing values. The thesis includes a critical comparison with related systems such as PROTOS, PEBM and AUTOCLASS. The results show that: (a) the model is able to identify exemplars that lead to a classification accuracy comparable to published results with other methods on most of the chosen datasets; (b) most of the datasets can be represented well by a relatively small number of exemplars which are identified by FReBE; (c) FReBE shows that family resemblance can be used as a principle for finding good exemplars.

Item Type: Thesis (PhD)
Contributors: Vadera, S (Supervisor)
Schools: Schools > School of Computing, Science and Engineering
Depositing User: Institutional Repository
Date Deposited: 19 Aug 2021 10:40
Last Modified: 27 Aug 2021 21:57
URI: http://usir.salford.ac.uk/id/eprint/61650

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