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.
|Number of pages||10|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|Publication status||Published - 2006 Nov|
Bibliographical noteFunding Information:
This work was partly supported by the JSPS Postdoctoral Fellowship Program and the Okumura Group at Tokyo Institute of Technology. H.-C. Rim was the corresponding author.
- Poisson model
- Text classification
- feature weighting
- naive Bayes classifier
ASJC Scopus subject areas
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics