A ROBUST ENSEMBLE LEARNING USING ZERO-ONE LOSS FUNCTION

• Sano Natsuki

  Central Research Institute of Electric Power Industry

• Suzuki Hideo

  University of Tsukuba

• Koda Masato

  University of Tsukuba

Abstract

Classifier is used for pattern recognition in various fields including data mining. Boosting is an ensemble learning method to boost (enhance) an accuracy of single classifier. We propose a new, robust boosting method by using a zero-one step function as a loss function. In deriving the method, the MarginBoost technique is blended with the stochastic gradient approximation algorithm, called Stochastic Noise Reaction (SNR). Based on intensive numerical experiments, we show that the proposed method is actually better than AdaBoost on 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 51 (1), 95-110, 2008

  The Operations Research Society of Japan

Keywords

• Data Analysis
• Data mining
• AdaBoost
• Zero-one Loss Function
• Stochastic Noise Reaction

Details

• CRID: 1390001204109164672
• NII Article ID: 110006632507
• NII Book ID: AA00703935
• DOI: 10.15807/jorsj.51.95
• ISSN: 21888299
• ISSN: 04534514
• NDL BIB ID: 9421038
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I9421038
• Web Site: https://www.jstage.jst.go.jp/article/jorsj/51/1/51_KJ00004879571/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed