Location of Repository

Boosting and Rocchio Applied to Text Filtering

By Robert Schapire, Yoram Singer and Amit Singhal

Abstract

We discuss two learning algorithms for text filtering: modified Rocchio and a boosting algorithm called AdaBoost. We show how both algorithms can be adapted to maximize any general utility matrix that associates cost (or gain) for each pair of machine prediction and correct label. We first show that AdaBoost significantly outperforms another highly effective text filtering algorithm. We then compare AdaBoost and Rocchio over three large text filtering tasks. Overall both algorithms are comparable and are quite effective. AdaBoost produces better classifiers than Rocchio when the training collection contains a very large number of relevant documents. However, on these tasks, Rocchio runs much faster than AdaBoost. 1 Introduction With the explosion in the amount of information available electronically, information filtering systems that automatically send articles of potential interest to a user are becoming increasingly important. If users indicate their interests to a filtering system..

Year: 1998
OAI identifier: oai:CiteSeerX.psu:10.1.1.32.9522
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://dnkweb.denken.or.jp/boo... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.