Automated feature selection is important for text categorization to reduce feature size and to
speed up learning process of classifiers. In this paper, we present a novel and efficient …

As an important preprocessing technology in text classification, feature selection can
improve the scalability, efficiency and accuracy of a text classifier. In general, a good feature …

There are numerous text documents available in electronic form. More and more are
becoming available every day. Such documents represent a massive amount of information …

Naïve Bayes classifiers which are widely used for text classification in machine learning are
based on the conditional probability of features belonging to a class, which the features are …

Feature weighting is used to alleviate the conditional independence assumption of Naïive
Bayes text classifiers and consequently improve their generalization performance. Most …

This paper works on feature weighting approaches for naive Bayes text classifiers. Almost all
existing feature weighting approaches for naive Bayes text classifiers have some defects …

In this paper, we present a Bayesian classification approach for automatic text categorization
using class-specific features. Unlike conventional text categorization approaches, our …

Naive Bayes (NB) is one of the top 10 algorithms thanks to its simplicity, efficiency, and
interpretability. To weaken its attribute independence assumption, naive Bayes tree …

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms due to its
simplicity, efficiency and efficacy. Of numerous proposals to improve the accuracy of naive …

Most previous works of feature selection emphasized only the reduction of high
dimensionality of the feature space. But in cases where many features are highly redundant …