Robust boosting algorithm against mislabeling in multiclass problems.
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.
Takenouchi
Takashi
T
Nara Institute of Science and Technology, Graduate School of Information Science, Ikoma, Nara 630-0192, Japan. ttakashi@is.naist.jp
Eguchi
Shinto
S
Murata
Noboru
N
Kanamori
Takafumi
T
D000465:Algorithms; D001185:Artificial Intelligence; D002965:Classification / Q000379:methods; D015233:Models, Statistical; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics