A unified framework of binary classifiers ensemble for multi-class classification

Authors:
Takashi Takenouchi;Shin Ishii
Affiliations:
Future University Hakodate, Hakodate, Hokkaido, Japan;Graduate School of Informatics, Kyoto University, Uji, Kyoto, Japan
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

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Abstract

We present a novel methods for multi-class classification by ensemble of binary classifiers for multi-class classification. The proposed method is characterized by a minimization problem of weighted divergences, and includes a lot of conventional methods as special cases. We discuss relationship between the proposed method and conventional methods and statistical properties of the proposed method. A small experiment shows that the proposed method can effectively incorporate information of multiple binary classifiers into multi-class classifier.