Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14577
Title: Investigation of bayesian networks for classification problems involving binary data
Authors: LIM BOON LEONG (S7628766Z)
Keywords: Bayesian Networks, Machine Learning, Binary Data
Issue Date: 23-Mar-2005
Source: LIM BOON LEONG (S7628766Z) (2005-03-23). Investigation of bayesian networks for classification problems involving binary data. ScholarBank@NUS Repository.
Abstract: In this dissertation, the main focus is on Bayesian Networks. In particular, we are examining the effectiveness of Na??ve Bayes, Tree Augmented Na??ve Bayes and Bayesian Networks in terms of their performance on the eventual prediction on test set data. This work looks at the various complexities of the three Networks. The criterion for the type of data used is that they should model those in the real world. Hence, two characteristics are determined to be representative of real world data a?? limited training examples and data corrupted with noise. To assist understanding of the results, binary data is used. It is found that under certain conditions, a simple algorithm like Na??ve Bayes performs better even though the underlying network structure is more complex. A real world dataset is used to verify the results. Finally, a systematic method is developed to tackle real world binary data.
URI: http://scholarbank.nus.edu.sg/handle/10635/14577
Appears in Collections:Master's Theses (Open)

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