Robust boosting algorithm against mislabeling in multiclass problems

Takashi Takenouchi*, Shinto Eguchi, Noboru Murata, Takafumi Kanamori

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)


We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchanged for others. For the two boosting algorithms, theoretical aspects supporting the robustness for mislabeling are explored.We apply the proposed two boosting methods for synthetic and real data sets to investigate the performance of thesemethods, focusing on robustness, and confirm the validity of the proposed methods.

Original languageEnglish
Pages (from-to)1596-1630
Number of pages35
JournalNeural Computation
Issue number6
Publication statusPublished - 2008 Jun

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Cognitive Neuroscience


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