Robust boosting algorithm against mislabeling in multiclass problems.

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.

journal_name

Neural Comput

journal_title

Neural computation

authors

Takenouchi T,Eguchi S,Murata N,Kanamori T

doi

10.1162/neco.2007.11-06-400

subject

Has Abstract

pub_date

2008-06-01 00:00:00

pages

1596-630

issue

6

eissn

0899-7667

issn

1530-888X

journal_volume

20

pub_type

信件
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