Title :
Classifying RSS Feeds with an Artificial Immune System
Author :
Burkepile, Adam ; Fizzano, Perry
Author_Institution :
Dept Of Comput. Sci., Western Washington Univ., Bellingham, WA, USA
Abstract :
Artificial Immune Systems (AIS) have been used in a number of applications from autonomous navigation to computer security because of their ability to rapidly adapt and evolve. In this paper we examine the application of an AIS for the purpose of determining which news articles from a set of RSS feeds are relevant. Because the articles we are examining come from RSS feeds, the articles can vary greatly in length and detail. Our training set is composed of a set of news articles that represent articles a user has already deemed relevant. Then we have the AIS determine which articles from another set are related to the relevant articles. We show that the AIS performs well regardless of the diversity of the subjects in the data set and can even make fairly fine grained distinctions with high accuracy.
Keywords :
XML; artificial immune systems; file organisation; information filtering; learning (artificial intelligence); pattern classification; security of data; RSS feed classification; XML file; artificial immune system; autonomous navigation; computer security; machine learning; really simple syndication; Application software; Artificial immune systems; Computer science; Feeds; Immune system; Information filtering; Information filters; Internet; Knowledge management; Navigation; AIS; Artificial Immune System; Classification;
Conference_Titel :
Information, Process, and Knowledge Management, 2010. eKNOW '10. Second International Conference on
Conference_Location :
Saint Maarten
Print_ISBN :
978-1-4244-5688-8
DOI :
10.1109/eKNOW.2010.19
