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 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 …

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 …

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 …

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 …

In this paper, we propose a filtering method for feature selection called ALOFT (At Least One
FeaTure). The proposed method focuses on specific characteristics of text categorization …

To improve scalability of text categorization and reduce over-fitting, it is desirable to reduce
the number of words used for categorisiation. Further, it is desirable to achieve such a goal …

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 …

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 …