A robust ensemble learning using zero-one loss function

Natsuki Sano, Hideo Suzuki, Masato Koda

研究成果: Article査読

1 被引用数 (Scopus)

抄録

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.

本文言語English
ページ(範囲)95-110
ページ数16
ジャーナルJournal of the Operations Research Society of Japan
51
1
DOI
出版ステータスPublished - 2008 3月
外部発表はい

ASJC Scopus subject areas

  • 決定科学(全般)
  • 経営科学およびオペレーションズ リサーチ

フィンガープリント

「A robust ensemble learning using zero-one loss function」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル