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. © 2012 Springer-Verlag.
CITATION STYLE
Takenouchi, T., & Ishii, S. (2012). A unified framework of binary classifiers ensemble for multi-class classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7664 LNCS, pp. 375–382). https://doi.org/10.1007/978-3-642-34481-7_46
Mendeley helps you to discover research relevant for your work.