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We evaluate empirically a scheme for combining classifiers, known as stacked generalization, in the context of anti-spam filtering, a novel cost-sensitive application of text categorization. Unsolicited commercial e-mail, or spam, floods mailboxes, causing frustration, wasting bandwidth, and exposing minors to unsuitable content. Using a public corpus, we show that stacking can improve the efficiency of automatically induced anti-spam filters, and that such filters can be used in real-life applications.
The growing problem of unsolicited bulk e-mail, also known as spam, has generated a need for reliable anti-spam e-mail filters. Filters of this type have so far been based mostly on manually constructed keyword patterns. An alternative approach has r
It has recently been argued that a Naive Bayesian classifier can be used to filter unsolicited bulk e-mail (spam). We conduct a thorough evaluation of this proposal on a corpus that we make publicly available, contributing towards standard benchmarks
We investigate the performance of two machine learning algorithms in the context of anti-spam filtering. The increasing volume of unsolicited bulk e-mail (spam) has generated a need for reliable anti-spam filters. Filters of this type have so far bee
A basic component in Internet applications is the electronic mail and its various implications. The paper proposes a mechanism for automatically classifying emails and create dynamic groups that belong to these messages. Proposed mechanisms will be b
The task of identifying out-of-domain (OOD) input examples directly at test-time has seen renewed interest recently due to increased real world deployment of models. In this work, we focus on OOD detection for natural language sentence inputs to task