Nardiello P., Sebastiani F., Sperduti A.
Text categorization
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 speci.cally 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 paper we address the problem of exploiting the potential of weighted representations in the context of AdaBoost-like algorithms by discretizing the continuous attributes through the application of entropybased discretization methods. We present experimental results on the Reuters-21578 text categorization collection, showing that for both algorithms the version with discretized continuous attributes outperforms the version with traditional binary representations.
Source: Lecture notes in computer science 2633 (2003): 320–334.
Publisher: Springer, Berlin , Germania
@article{oai:it.cnr:prodotti:44074, title = {Discretizing continuous attributes in AdaBoost for text categorization}, author = {Nardiello P. and Sebastiani F. and Sperduti A.}, publisher = {Springer, Berlin , Germania}, journal = {Lecture notes in computer science}, volume = {2633}, pages = {320–334}, year = {2003} }