Naive Bayes text classifiers: a locally weighted learning approach
- 1 June 2013
- journal article
- research article
- Published by Taylor & Francis Ltd in Journal of Experimental & Theoretical Artificial Intelligence
- Vol. 25 (2), 273-286
- https://doi.org/10.1080/0952813x.2012.721010
Abstract
Due to being fast, easy to implement and relatively effective, some state-of-the-art naive Bayes text classifiers with the strong assumption of conditional independence among attributes, such as multinomial naive Bayes, complement naive Bayes and the one-versus-all-but-one model, have received a great deal of attention from researchers in the domain of text classification. In this article, we revisit these naive Bayes text classifiers and empirically compare their classification performance on a large number of widely used text classification benchmark datasets. Then, we propose a locally weighted learning approach to these naive Bayes text classifiers. We call our new approach locally weighted naive Bayes text classifiers (LWNBTC). LWNBTC weakens the attribute conditional independence assumption made by these naive Bayes text classifiers by applying the locally weighted learning approach. The experimental results show that our locally weighted versions significantly outperform these state-of-the-art naive Bayes text classifiers in terms of classification accuracy.Keywords
This publication has 14 references indexed in Scilit:
- DISCRIMINATIVELY WEIGHTED NAIVE BAYES AND ITS APPLICATION IN TEXT CLASSIFICATIONInternational Journal on Artificial Intelligence Tools, 2012
- One Dependence Value Difference MetricKnowledge-Based Systems, 2011
- Random one-dependence estimatorsPattern Recognition Letters, 2011
- DECISION TREE WITH BETTER CLASS PROBABILITY ESTIMATIONInternational Journal of Pattern Recognition and Artificial Intelligence, 2009
- Improvements to Platt's SMO Algorithm for SVM Classifier DesignNeural Computation, 2001
- Centroid-Based Document Classification: Analysis and Experimental ResultsLecture Notes in Computer Science, 2000
- 10.1162/153244303322753670Applied Physics Letters, 2000
- Bayesian Network ClassifiersMachine Learning, 1997
- Locally Weighted LearningArtificial Intelligence Review, 1997
- On the Optimality of the Simple Bayesian Classifier under Zero-One LossMachine Learning, 1997