Please use this identifier to cite or link to this item:
Title: Enhancing SNNB with local accuracy estimation and ensemble techniques
Authors: Xie, Z.
Zhang, Q.
Hsu, W. 
Lee, M.L. 
Issue Date: 2005
Source: Xie, Z.,Zhang, Q.,Hsu, W.,Lee, M.L. (2005). Enhancing SNNB with local accuracy estimation and ensemble techniques. Lecture Notes in Computer Science 3453 : 523-535. ScholarBank@NUS Repository.
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. © Springer-Verlag Berlin Heidelberg 2005.
Source Title: Lecture Notes in Computer Science
ISSN: 03029743
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Jan 20, 2018

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.