Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Network with no dependencies between words and binary word features (e

The naive Bayes text classifier has long been a core technique in information retrieval and, more recently, it has emerged as a focus of research itself in machine learning. This paper is concerned with the naive Bayes text classifier in its multinomial model instantiation.

The Naive Bayes classifier exists in different versions. One version, called multi-variate Bernoulli or binary independence model, uses binary word occurrence vectors, while the multinomial model uses word frequency counts. Many publications cite this difference as the main reason

Recent work in text classification has used two different first-order probabilistic models for classification, both of which make the naive Bayes assumption. Some use a multi-variate Bernoulli model, that is, a Bayesian Net-work with no dependencies between words and binary word features (e

the relationships between terms, offers a rather poor docu-ment representation. Some previous research has shown that incorporat-ing language models into the naive Bayes classifier (NBC) can improve the performance of text classification. Although the widely used N-gram language models (LM) can exploit

We define a new feature selection score for text classification based on the KL-divergence between the distribution of words in training documents and their classes. The score favors words that have a similar distribution in documents of the same class but different distributions in documents

The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump- tions made

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

This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large

were used as baselines, since they were evaluated on all versions of Reuters that exclude the unlabelled documents. As a global observation, kNN, LLSF and a neural network method had the best performance; except for a Naive Bayes approach, the other learning algorithms also performed relatively well.