Abstract
Extended naive Bayesian classifier (ENBC) is a general framework of NBCs, which is developed based on \(t\)-norm based ordered weighted averaging (\(t\)-OWA) operator and uses the weighted summation of products of margin probabilities to determine class-conditional probability. Since ENBC was proposed in 2006, there is no such a study which tests the performances of ENBC on the real classification datasets. Thus, in this paper we conduct an experimental investigation to ENBC’s generalization capability based on 44 benchmark KEEL and UCI datasets. The analysis shows that (1) ENBC is instable and its aggregation weights are sensitive to the order of training samples and (2) ENBC indeed has higher generalization capability than the existing NBCs, e.g., normal naive Bayesian and flexible naive Bayesian when its weight is properly selected.
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Notes
Strictly speaking, Eqs. (3) and (4) are not correct. Regarding this issue, please refer to the footnote on page 339 of John and Langley’s work [7].
For the meanings of mathematical symbols \(h_1\), \(h_2\), \(s_2\), \(t_1\), \(t_2\), \(t_3\), \(u_1\), and \(u_2\), etc., please refer to the table on page 585 of Yager’s work [19].
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The authors are very grateful for the editors and two anonymous reviewers. Their valuable and constructive comments and suggestions helped us significantly improve this work.
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Chen, Ss., Cao, Jj., Gan, Ll. et al. Experimental study on generalization capability of extended naive Bayesian classifier. Int. J. Mach. Learn. & Cyber. 9, 5–19 (2018). https://doi.org/10.1007/s13042-014-0311-8
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DOI: https://doi.org/10.1007/s13042-014-0311-8