Averaged boosting: a noise-robust ensemble method

Authors:
Yongdai Kim
Affiliations:
Department of Statistics, Ewha Womans University, Seoul, Korea
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

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Abstract

A new noise robust ensemble method called "Averaged Boosting (A-Boosting)" is proposed. Using the hypothetical ensemble algorithm in Hilbert space, we explain that A-Boosting can be understood as a method of constructing a sequence of hypotheses and coefficients such that the average of the product of the base hypotheses and coefficients converges to the desirable function. Empirical studies showed that A-Boosting outperforms Bagging for low noise cases and is more robust than AdaBoost to label noise.