Artificial immune system for attribute weighted Naive Bayes classification

Wu, J; Cai, Z; Zeng, S; Zhu, X

Artificial immune system for attribute weighted Naive Bayes classification

Wu, J Cai, Z Zeng, S Zhu, X

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the International Joint Conference on Neural Networks, 2013
Issue Date:: 2013-12-01

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Cai, Z	en_US
dc.contributor.author	Zeng, S	en_US
dc.contributor.author	Zhu, X	en_US
dc.date.issued	2013-12-01	en_US
dc.identifier.citation	Proceedings of the International Joint Conference on Neural Networks, 2013	en_US
dc.identifier.isbn	9781467361293	en_US
dc.identifier.uri	http://hdl.handle.net/10453/27207
dc.description.abstract	Naive Bayes (NB) is a popularly used classification method. One potential weakness of NB is the strong conditional independence assumption between attributes, which may deteriorate the classification accuracy. In this paper, we propose a new Artificial Immune System based Weighted Naive Bayes (AISWNB) classifier. AISWNB uses immunity theory in artificial immune systems to find optimal weight values for each attribute. The adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. Because AISWNB uses artificial immune system search mechanism to find optimal weights, it does not need to know the importance of individual attributes nor the relevance among attributes. As a result, it can obtain optimal weight value for each attribute during the learning process. Experiments and comparisons on 36 benchmark data sets demonstrate that AISWNB outperforms other state-of-the-art attribute weighted NB algorithms. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the International Joint Conference on Neural Networks	en_US
dc.relation.isbasedon	10.1109/IJCNN.2013.6706818	en_US
dc.title	Artificial immune system for attribute weighted Naive Bayes classification	en_US
dc.type	Conference Proceeding
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	Dallas, TX, USA	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 - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	in_progress
pubs.publication-status	Published	en_US

Abstract:

Naive Bayes (NB) is a popularly used classification method. One potential weakness of NB is the strong conditional independence assumption between attributes, which may deteriorate the classification accuracy. In this paper, we propose a new Artificial Immune System based Weighted Naive Bayes (AISWNB) classifier. AISWNB uses immunity theory in artificial immune systems to find optimal weight values for each attribute. The adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. Because AISWNB uses artificial immune system search mechanism to find optimal weights, it does not need to know the importance of individual attributes nor the relevance among attributes. As a result, it can obtain optimal weight value for each attribute during the learning process. Experiments and comparisons on 36 benchmark data sets demonstrate that AISWNB outperforms other state-of-the-art attribute weighted NB algorithms. © 2013 IEEE.

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http://hdl.handle.net/10453/27207