A robust boosting method for mislabeled data

Natsuki Sano, Hideo Suzuki, Masato Koda

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

We propose a new, robust boosting method by using a siginoidal function as a loss function. In deriving the method, the stagewise additive modelling methodology is blended with the gradient descent algorithms. Based on intensive numerical experiments, we show that the proposed method is actually better than AdaBoost and other regularized method in test error rates in the case of noisy, mislabeled situation.

Original languageEnglish
Pages (from-to)182-196
Number of pages15
JournalJournal of the Operations Research Society of Japan
Volume47
Issue number3
DOIs
Publication statusPublished - 2004 Sep
Externally publishedYes

Keywords

  • AdaBoost
  • Boosting
  • Data analysis
  • Data mining
  • Machine learning
  • Sigmoidal function

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Management Science and Operations Research

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