Robust boosting algorithm against mislabeling in multiclass problems

Authors:
Takashi Takenouchi;Shinto Eguchi;Noboru Murata;Takafumi Kanamori
Affiliations:
Nara Institute of Science and Technology, Graduate School of Information Science, Ikoma, Nara 630-0192, Japan, ttakashi@is.naist.jp;Institute of Statistical Mathematics, Japan and Department of Statistical Science, Graduate University of Advanced Studies, Minami-azabu, Tokyo 106-8569, Japan, eguchi@ism.ac.jp;Faculty of Science and Engineering, Waseda University, Shinjuku, Tokyo 169-8555, Japan, noboru.murata@eb.waseda.ac.jp;Department of Computer Science and Mathematical Informatics, Graduate School of Information Science, Nagoya University, Furocho, Chikusaku, Nagoya 464-8603, Japan, kanamori@is.nagoya-u.ac.jp
Venue:
Neural Computation
Year:
2008

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Abstract

We discuss robustness against mislabeling in multiclass labels for classification problems and propose two algorithms of boosting, the normalized Eta-Boost.M and Eta-Boost.M, based on the Eta-divergence. Those two boosting algorithms are closely related to models of mislabeling in which the label is erroneously exchanged for others. For the two boosting algorithms, theoretical aspects supporting the robustness for mislabeling are explored. We apply the proposed two boosting methods for synthetic and real data sets to investigate the performance of these methods, focusing on robustness, and confirm the validity of the proposed methods.