Many feature selection methods have been proposed for text categorization. However, their
performances are usually verified by experiments, so the results rely on the corpora used …

Text categorisation relies heavily on feature selection. Both the possible reduction in
dimensionality as well as improvements in classification performance are highly desirable …

Feature selection has been an interesting issue in text categorization up to now. Previous
works in feature selection often used filter model in which features, after ranked by a …

Feature selection is known as a good solution to the high dimensionality of the feature space
and mostly preferred feature selection methods for text classification are filter-based ones. In …

Text categorization (TC) is a problem of assigning a document into predefined classes. One
of the most important issues in TC is feature selection. In this paper, we propose a new …

Feature selection (FS) is aimed at reducing the size of the feature space. The advantage of
FS is, in general, two-fold: improved accuracy of the learned classifiers, and efficiency of the …

In this paper, we propose a new feature-selection algorithm for text classification, called best
terms (BT). The complexity of BT is linear in respect to the number of the training-set …

This paper proposes a new feature selection score for text classification. The value that this
score assigns to each feature has an appealing Bayesian interpretation, being the posterior …

Feature selection plays a vital role in text categorisation. A range of different methods have
been developed, each having unique properties and selecting different features. We show …

A common approach in text categorization is to represent each word as a feature, however,
many of these features are irrelevant. So, dimensionality reduction is an important step to …