A ROBUST BOOSTING METHOD FOR MISLABELED DATA

• Sano Natsuki

  University of Tsukuba

• Suzuki Hideo

  University of Tsukuba

• Koda Masato

  University of Tsukuba

Bibliographic Information

Other Title

• Robust Boosting Method for Mislabeled Data

Abstract

We propose a new, robust boosting method by using a sigmoidal 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.

Journal

• Journal of the Operations Research Society of Japan

  Journal of the Operations Research Society of Japan 47 (3), 182-196, 2004

  The Operations Research Society of Japan

Keywords

• Data analysis
• data mining
• machine learning
• boosting
• AdaBoost
• sigmoidal function

Details

• CRID: 1390282679087345920
• NII Article ID: 110001183596
• NII Book ID: AA00703935
• DOI: 10.15807/jorsj.47.182
• ISSN: 21888299
• ISSN: 04534514
• NDL BIB ID: 7064832
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I7064832
• Web Site: https://www.jstage.jst.go.jp/article/jorsj/47/3/47_KJ00000757451/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed