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 …

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 …

This paper presents a novel local feature selection approach for text categorization. It
constructs a feature set for each category by first selecting a set of terms highly indicative of …

Supervised text categorization is a machine learning task where a predefined category label
is automatically assigned to a previously unlabelled document based upon characteristics of …

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 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 …

Feature selection is an important research issue in text categorization. The reason for this is
that thousands of features are often involved, even when the simplest document …

A common way of performing Feature Selection in Text Categorization consists in keeping
the features with highest score according to certain measures, like linear ones which have …

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 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 …