Some Effective Techniques for Naive Bayes Text Classification

Citations Over TimeTop 10% of 2006 papers

Abstract

While naive Bayes is quite effective in various data mining tasks, it shows a disappointing result in the automatic text classification problem. Based on the observation of naive Bayes for the natural language text, we found a serious problem in the parameter estimation process, which causes poor results in text classification domain. In this paper, we propose two empirical heuristics: per-document text normalization and feature weighting method. While these are somewhat ad hoc methods, our proposed naive Bayes text classifier performs very well in the standard benchmark collections, competing with state-of-the-art text classifiers based on a highly complex learning method such as SVM.

Related Papers

• → Robust Approach for Estimating Probabilities in Naive-Bayes Classifier(2007)24 cited
• Analysis on Text Classification Using Naive Bayes(2007)
• → Iterative Feature Selection Using Information Gain & Naïve Bayes for Document Classification(2018)1 cited
• Error estimation in a naive bayes classifier(2008)
• Adaptive adjustment weighted text classification(2011)