A boosting algorithm for label covering in multilabel problems

Yonatan Amit, Ofer Dekel, Yoram Singer

Research output: Contribution to journalConference articlepeer-review

10 Scopus citations


We describe, analyze and experiment with a boosting algorithm for multilabel categorization problems. Our algorithm includes as special cases previously studied boosting algorithms such as Adaboost.MH. We cast the multilabel problem as multiple binary decision problems, based on a user-defined covering of the set of labels. We prove a lower bound on the progress made by our algorithm on each boosting iteration and demonstrate the merits of our algorithm in experiments with text categorization problems.

Original languageEnglish (US)
Pages (from-to)27-34
Number of pages8
JournalJournal of Machine Learning Research
StatePublished - 2007
Event11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007 - San Juan, Puerto Rico
Duration: Mar 21 2007Mar 24 2007

All Science Journal Classification (ASJC) codes

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


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