Abstract
Several classification algorithms based on local naïve Bayesian rules have been recently developed to provide high predictability. However, most of them use classifier selection strategy in decision making. To make use of classifier fusion strategy, this paper investigates a boosting algorithm for local naïve Bayesian rules. Firstly, we develop an algorithmic framework as a forward stage-wise additive model. Then, a construction algorithm for lazy naïve Bayesian rules is designed to materialize the algorithmic framework. The construction algorithm starts from the most general rule, and uses a greedy search to grow the antecedent repeatedly in order to get a better rule at each step. Experimental results show that the proposed method has successfully reduced the overall error rate on a variety of domains, compared with boosted naïve Bayesian classifier, and lazy Bayesian rule algorithm.
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Xie, Z. (2009). Boosting Local Naïve Bayesian Rules. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_87
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DOI: https://doi.org/10.1007/978-3-642-01510-6_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
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