title: Robust loss functions for boosting.
abstract: Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, the truncation of loss functions is applied to contamination models that describe the occurrence of mislabels near decision boundaries. Numerical experiments illustrate that the proposed loss functions derived from the contamination models are useful for handling highly noisy data in comparison with other loss functions.
authors.affiliation: Department of Mathematical and Computing Sciences, Tokyo Institute of Technology, Tokyo 152-8552, Japan. kanamori@is.titech.ac.jp
authors.forename: Takafumi
authors.initials: T
authors.lastname: Kanamori
authors.forename: Takashi
authors.initials: T
authors.lastname: Takenouchi
authors.forename: Shinto
authors.initials: S
authors.lastname: Eguchi
authors.forename: Noboru
authors.initials: N
authors.lastname: Murata
mesh_terms: D000465:Algorithms; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*