Discretizing Continuous Attributes in AdaBoost for Text Categorization

Abstract. We focus on two recently proposed algorithms in the family of “boosting”-based learners for automated text classification, AdaBoost.MH and AdaBoost.MHKR . While the former is a realization of the well-known AdaBoost algorithm specifically aimed at multi-label text categorization, the latter is a generalization of the former based on the idea of learning a committee of classifier sub-committees. Both algorithms have been among the best performers in text categorization experiments so far. A problem in the use of both algorithms is that they require documents to be represented by binary vectors, indicating presence or absence of the terms in the document. As a consequence, these algorithms cannot take full advantage of the “weighted” representations (consisting of vectors of continuous attributes) that are customary in information retrieval tasks, and that provide a much more significant rendition of the document’s content than binary representations. In this pape...

• Type: Conference
• Year: 2003
• Where: ECIR
• Authors: Pio Nardiello, Fabrizio Sebastiani, Alessandro Sperduti
• Keywords: Algorithms, Continuous Attributes, ECIR 2003, Information Technology, Text Categorization