A robust boosting method for mislabeled data

Natsuki Sano; Hideo Suzuki; Masato Koda

doi:10.15807/jorsj.47.182

A robust boosting method for mislabeled data

Natsuki Sano, Hideo Suzuki, Masato Koda

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	182-196
Number of pages	15
Journal	Journal of the Operations Research Society of Japan
Volume	47
Issue number	3
DOIs	https://doi.org/10.15807/jorsj.47.182
Publication status	Published - 2004 Sept
Externally published	Yes

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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10.15807/jorsj.47.182

Cite this

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AB - 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.

KW - AdaBoost

KW - Boosting

KW - Data analysis

KW - Data mining

KW - Machine learning

KW - Sigmoidal function

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A robust boosting method for mislabeled data

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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