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.
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Takenouchi, T., Ishii, S. (2012). A Unified Framework of Binary Classifiers Ensemble for Multi-class Classification. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_46
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DOI: https://doi.org/10.1007/978-3-642-34481-7_46
Publisher Name: Springer, Berlin, Heidelberg
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