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An empirical framework for selecting the best Bayesian classifier

Moran, Stuart; He, Yulan and Liu, Kecheng (2009). An empirical framework for selecting the best Bayesian classifier. In: International Conference on Data Mining and Knowledge Engineering, 01-03 July 2009, London.

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Abstract

Data miners have access to a significant number of classifiers and use them on a variety of different types of dataset. This large selection makes it difficult to know which classifier will perform most effectively in any given case. Usually an understanding of learning algorithms is combined with detailed domain knowledge of the dataset at hand to lead to the choice of a classifier. We propose an empirical framework that quantitatively assesses the accuracy of a selection of classifiers on different datasets, resulting in a set of classification rules generated by the J48 decision tree algorithm. Data miners can follow these rules to select the most effective classifier for their work. By optimising the parameters used for learning and the sampling techniques applied, a set of rules were learned that select with 78% accuracy (with 0.5% classification accuracy tolerance), the most effective classifier.

Item Type: Conference Item
Copyright Holders: 2009 International Association of Engineers
Extra Information: Proceedings of the World Congress on Engineering 2009
Editors: S. I. Ao and Len Gelman and David WL Hukins and Andrew Hunter and A. M. Korsunsky

ISBN of Vol I (pp1-927): 978-988-17012-5-1

ISBN of Vol II (pp928-1895): 978-988-18210-1-0

Publisher: Newswood Limited

Organization: International Association of Engineers
Keywords: Bayesian networks; data mining; classification; search algorithm; decision tree
Academic Unit/Department: Knowledge Media Institute
Interdisciplinary Research Centre: Centre for Research in Computing (CRC)
Related URLs:
Item ID: 25093
Depositing User: Yulan He
Date Deposited: 07 Dec 2010 09:04
Last Modified: 26 Oct 2012 04:37
URI: http://oro.open.ac.uk/id/eprint/25093
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