A theory of multiclass boosting

Indraneel Mukherjee, Robert E. Schapire

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

Boosting combines weak classifiers to form highly accurate predictors. Although the case of binary classification is well understood, in the multiclass setting, the "correct" requirements on the weak classifier, or the notion of the most efficient boosting algorithms are missing. In this paper, we create a broad and general framework, within which we make precise and identify the optimal requirements on the weak-classifier, as well as design the most effective, in a certain sense, boosting algorithms that assume such requirements.

Original languageEnglish (US)
Pages (from-to)437-497
Number of pages61
JournalJournal of Machine Learning Research
Volume14
Issue number1
StatePublished - Feb 2013
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability

Keywords

  • Boosting
  • Drifting games
  • Multiclass
  • Weak learning condition

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