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Boosting and Rocchio Applied to Text Filtering (1998) [83 citations — 2 self]

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 It is becoming increasingly hard to cope with the explosion of electronic information that is now available. Information filtering systems that automatically send articles of potential interest are becoming a necessity in this information age. Typically users i...

Citations

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