Using Laplace and angular measures for Feature Selection in Text Categorisation
by Elena Montanes, Pedro Alonso, Elias F. Combarro, Irene Diaz, Raquel Cortina, Jose Ranilla
International Journal of Advanced Intelligence Paradigms (IJAIP), Vol. 1, No. 1, 2008

Abstract: Text Categorisation (TC) consists of automatically assigning documents to a set of prefixed categories. It usually involves the management of a huge number of features. Some of them are irrelevant or noisy which mislead the classifiers. Thus, they are reduced to increase the efficiency and effectiveness of the classification. In this paper we propose to select relevant features using two different families of filtering measures, which are simpler than other usual measures applied for this purpose. The experiments over three corpora show that, in general, the proposed measures perform equal or better than the existing ones, sometimes allowing greater reductions.

Online publication date: Fri, 17-Oct-2008

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Advanced Intelligence Paradigms (IJAIP):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com