A study and development of Bayesian exemplar based models

Wu, J

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.

• Thesis Type: Thesis
• Deposit Date: Aug 19, 2021
• Award Date: May 1, 2008