Some Effective Techniques for Naive Bayes Text Classification

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 re...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2006-11, Vol.18 (11), p.1457-1466
Main Authors: KIM, Sang-Bum, HAN, Kyoung-Soo, RIM, Hae-Chang, SUNG HYON MYAENG
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Bayesian analysis
Classification
Classifiers
Collection
Computer science
control theory
systems
Data mining
Exact sciences and technology
feature weighting
Frequency
Information systems. Data bases
Learning
Learning systems
Memory organisation. Data processing
naive Bayes classifier
Natural languages
Parameter estimation
Poisson model
Probability
Software
Speech and sound recognition and synthesis. Linguistics
Statistical learning
Support vector machine classification
Support vector machines
Tasks
Text categorization
Text classification
Texts
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Summary: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
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2006.180