Boosting and Rocchio applied to text filtering

Robert E. Schapire, Yoram Singer, Amit Singhal

Research output: Contribution to journalConference articlepeer-review

210 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)215-223
Number of pages9
JournalSIGIR Forum (ACM Special Interest Group on Information Retrieval)
DOIs
StatePublished - 1998
EventProceedings of the 1998 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'98) - Melbourne, Vic., Aust
Duration: Aug 24 1998Aug 28 1998

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Hardware and Architecture

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