Locally weighted learning: How and when does it work in Bayesian networks?

Wu, J; Wu, B; Pan, S; Wang, H; Cai, Z

Locally weighted learning: How and when does it work in Bayesian networks?

Wu, J Wu, B Pan, S

Wang, H Cai, Z

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Publication Type:: Journal Article
Citation:: International Journal of Computational Intelligence Systems, 2015, 8 pp. 63 - 74
Issue Date:: 2015-01-01

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Field	Value	Language
dc.contributor.author	Wu, J	en_US
dc.contributor.author	Wu, B	en_US
dc.contributor.author	Pan, S https://orcid.org/0000-0003-0794-527X	en_US
dc.contributor.author	Wang, H	en_US
dc.contributor.author	Cai, Z	en_US
dc.date.issued	2015-01-01	en_US
dc.identifier.citation	International Journal of Computational Intelligence Systems, 2015, 8 pp. 63 - 74	en_US
dc.identifier.issn	1875-6891	en_US
dc.identifier.uri	http://hdl.handle.net/10453/121698
dc.description.abstract	© 2016, Taylor and Francis Ltd. All rights reserved. Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes, which may deteriorate the classification performance. One major approach to mitigate the BNC’s primary weakness (the attributes independence assumption) is the locally weighted approach. And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure. However, we do not know whether or how effective it works for improving the performance of the complex BNC. In this paper, we first do a survey on the complex structure models for BNCs and their improvements, then carry out a systematically experimental analysis to investigate the effectiveness of locally weighted method for complex BNCs, e.g., tree-augmented naive Bayes (TAN), averaged one-dependence estimators AODE and hidden naive Bayes (HNB), measured by classification accuracy (ACC) and the area under the ROC curve ranking (AUC). Experiments and comparisons on 36 benchmark data sets collected from University of California, Irvine (UCI) in Weka system demonstrate that locally weighting technologies just slightly outperforms unweighted complex BNCs on ACC and AUC. In other words, although locally weighting could significantly improve the performance of NB (a BNC with simple structure), it could not work well on BNCs with complex structures. This is because the performance improvements of BNCs are attributed to their structures not the locally weighting.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP140100545
dc.relation.ispartof	International Journal of Computational Intelligence Systems	en_US
dc.relation.isbasedon	10.1080/18756891.2015.1129579	en_US
dc.title	Locally weighted learning: How and when does it work in Bayesian networks?	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2016, Taylor and Francis Ltd. All rights reserved. Bayesian network (BN), a simple graphical notation for conditional independence assertions, is promised to represent the probabilistic relationships between diseases and symptoms. Learning the structure of a Bayesian network classifier (BNC) encodes conditional independence assumption between attributes, which may deteriorate the classification performance. One major approach to mitigate the BNC’s primary weakness (the attributes independence assumption) is the locally weighted approach. And this type of approach has been proved to achieve good performance for naive Bayes, a BNC with simple structure. However, we do not know whether or how effective it works for improving the performance of the complex BNC. In this paper, we first do a survey on the complex structure models for BNCs and their improvements, then carry out a systematically experimental analysis to investigate the effectiveness of locally weighted method for complex BNCs, e.g., tree-augmented naive Bayes (TAN), averaged one-dependence estimators AODE and hidden naive Bayes (HNB), measured by classification accuracy (ACC) and the area under the ROC curve ranking (AUC). Experiments and comparisons on 36 benchmark data sets collected from University of California, Irvine (UCI) in Weka system demonstrate that locally weighting technologies just slightly outperforms unweighted complex BNCs on ACC and AUC. In other words, although locally weighting could significantly improve the performance of NB (a BNC with simple structure), it could not work well on BNCs with complex structures. This is because the performance improvements of BNCs are attributed to their structures not the locally weighting.

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http://hdl.handle.net/10453/121698