Enhancing SNNB with local accuracy estimation and ensemble techniques

Authors:
Zhipeng Xie;Qing Zhang;Wynne Hsu;Mong Li Lee
Affiliations:
Department of Computing and Information Technology, Fudan University, Shanghai, P. R. China;Department of Computing and Information Technology, Fudan University, Shanghai, P. R. China;School of Computing, National University of Singapore, Singapore;School of Computing, National University of Singapore, Singapore
Venue:
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Year:
2005

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Abstract

Naïve Bayes, the simplest Bayesian classifier, has shown excellent performance given its unrealistic independence assumption. This paper studies the selective neighborhood-based naïve Bayes (SNNB) for lazy classification, and develops three variant algorithms, SNNB-G, SNNB-L, and SNNB-LV, all with linear computational complexity. The SNNB algorithms use local learning strategy for alleviating the independence assumption. The underlying idea is, for a test example, first to construct multiple classifiers on its multiple neighborhoods with different radius, and then to select out the classifier with the highest estimated accuracy to make decision. Empirical results show that both SNNB-L and SNNB-LV generate more accurate classifiers than naïve Bayes and several other state-of-the-art classification algorithms including C4.5, Naïve Bayes Tree, and Lazy Bayesian Rule. The SNNB-L and SNNB-LV algorithms are also computationally more efficient than the Lazy Bayesian Rule algorithm, especially on the domains with high dimensionality.