Toward Optimal Feature Selection in Naive Bayes for Text Categorization

On the Naive Bayes Model for Text Categorization

Text Categorization using Association Rule and Naive Bayes Classifier

As the amount of online text increases, the demand for text categorization to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic categorization of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained fro...

MMR-based Feature Selection for Text Categorization

We introduce a new method of feature selection for text categorization. Our MMR-based feature selection method strives to reduce redundancy between features while maintaining information gain in selecting appropriate features for text categorization. Empirical results show that MMR-based feature selection is more effective than Koller & Sahami’s method, which is one of greedy feature selection ...

Segmentation-based Feature Selection for Text Categorization

Text categorization is an interesting problem in artificial intelligence that gets more and more attention from researchers and industry. One central problem of text categorization is the selection of a good feature set. We propose a novel method for term selection for each category based on segmenting the documents belonging to a category into cohesive sub-parts that define the subtopics of th...

Poisson naive Bayes for text classification with feature weighting

In this paper, we investigate the use of multivariate Poisson model and feature weighting to learn naive Bayes text classifier. Our new naive Bayes text classification model assumes that a document is generated by a multivariate Poisson model while the previous works consider a document as a vector of binary term features based on the presence or absence of each term. We also explore the use of...