MP-boost: A multiple-pivot boosting algorithm and its application to text categorization

Andrea Esuli, Tiziano Fagni, Fabrizio Sebastiani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)

Abstract

ADABOOST.MH is a popular supervised learning algorithm for building multi-label (aka n-of-m) text classifiers. ADABOOST.MH belongs to the family of "boosting" algorithms, and works by iteratively building a committee of "decision stump" classifiers, where each such classifier is trained to especially concentrate on the document-class pairs that previously generated classifiers have found harder to correctly classify. Each decision stump hinges on a specific "pivot term", checking its presence or absence in the test document in order to take its classification decision. In this paper we propose an improved version of AD-ABOOST.MH, called MP-BOOST, obtained by selecting, at each iteration of the boosting process, not one but several pivot terms, one for each category. The rationale behind this choice is that this provides highly individualized treatment for each category, since each iteration thus generates, for each category, the best possible decision stump. We present the results of experiments showing that MP-BOOST is much more effective than ADABOOST.MH. In particular, the improvement in effectiveness is spectacular when few boosting iterations are performed, and (only) high for many such iterations. The improvement is especially significant in the case of macroaveraged effectiveness, which shows that MP-BOOST is especially good at working with hard, infrequent categories.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages1-12
Number of pages12
Volume4209 LNCS
Publication statusPublished - 2006
Externally publishedYes
Event13th International Conference on String Processing and Information Retrieval, SPIRE 2006 - Glasgow
Duration: 11 Oct 200613 Oct 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4209 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other13th International Conference on String Processing and Information Retrieval, SPIRE 2006
CityGlasgow
Period11/10/0613/10/06

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ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Esuli, A., Fagni, T., & Sebastiani, F. (2006). MP-boost: A multiple-pivot boosting algorithm and its application to text categorization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4209 LNCS, pp. 1-12). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4209 LNCS).