Attribute weighting via differential evolution algorithm for attribute Weighted Naive Bayes (WNB)

Wu, J; Cai, Z

Attribute weighting via differential evolution algorithm for attribute Weighted Naive Bayes (WNB)

Wu, J Cai, Z

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Publication Type:: Journal Article
Citation:: Journal of Computational Information Systems, 2011, 7 (5), pp. 1672 - 1679
Issue Date:: 2011-05-01

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Cai, Z	en_US
dc.date.issued	2011-05-01	en_US
dc.identifier.citation	Journal of Computational Information Systems, 2011, 7 (5), pp. 1672 - 1679	en_US
dc.identifier.issn	1553-9105	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121530
dc.description.abstract	The naive Bayes (NB) is a popular classification technique for data mining and machine learning, which is based on the attribute independence assumption. Researchers have proposed out many effective methods to improve the performance of NB by lowering its primary weakness-the assumption that attributes are independent given the class, such as backwards sequential elimination method, lazy elimination method and so on. Recently, Mark Hall presents a simple filter method for setting attribute weights for naive Bayes and proposes a decision tree-based attribute weighted method. In his paper, the experimental results show that the new weighted method performs better than other weighted methods. That weighting idea is taken as the objective of our study in which we use differential evolution algorithms to determine the weights of attributes and then use these weights in our previously developed Weighted Naïve Bayes (WNB). We evaluate the performance of new configuration (DE-WNB) on the whole 36 standard UCI data sets in Weka system. We also compare it with the decision tree-based attribute weighted methods and other methods mentioned in Mark Hall' paper for those data sets. Our experimental results show that the classification accuracy of our new algorithm DE-WNB is much higher than those of the other algorithms used to compare. The obtained classification accuracy is very good with respect to other common WNB classifiers in literature. Copyright © 2011 Binary Information Press.	en_US
dc.relation.ispartof	Journal of Computational Information Systems	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Attribute weighting via differential evolution algorithm for attribute Weighted Naive Bayes (WNB)	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	7	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	7	en_US

Abstract:

The naive Bayes (NB) is a popular classification technique for data mining and machine learning, which is based on the attribute independence assumption. Researchers have proposed out many effective methods to improve the performance of NB by lowering its primary weakness-the assumption that attributes are independent given the class, such as backwards sequential elimination method, lazy elimination method and so on. Recently, Mark Hall presents a simple filter method for setting attribute weights for naive Bayes and proposes a decision tree-based attribute weighted method. In his paper, the experimental results show that the new weighted method performs better than other weighted methods. That weighting idea is taken as the objective of our study in which we use differential evolution algorithms to determine the weights of attributes and then use these weights in our previously developed Weighted Naïve Bayes (WNB). We evaluate the performance of new configuration (DE-WNB) on the whole 36 standard UCI data sets in Weka system. We also compare it with the decision tree-based attribute weighted methods and other methods mentioned in Mark Hall' paper for those data sets. Our experimental results show that the classification accuracy of our new algorithm DE-WNB is much higher than those of the other algorithms used to compare. The obtained classification accuracy is very good with respect to other common WNB classifiers in literature. Copyright © 2011 Binary Information Press.

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http://hdl.handle.net/10453/121530