Abstract
Naive Bayes is often used in text classification applications and experiments because of its simplicity and effectiveness. However, many different versions of Bayes model consider only one aspect of a particular word. In this paper we define an information criterion, Projective Information Gain, to decide which representation is appropriate for a specific word. Based on this, the conditional independence assumption is extended to make it more efficient and feasible and then we propose a novel Bayes model, General Naive Bayes (GNB), which can handle two representations concurrently. Experimental results and theoretical justification that demonstrate the feasibility of our approach are presented.
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Wang, L., Cao, C., Li, X., Li, H. (2007). Finding the Optimal Feature Representations for Bayesian Network Learning. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_96
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DOI: https://doi.org/10.1007/978-3-540-71701-0_96
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
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