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Proceedings Paper

Mining discriminative class codes for multi-class classification based on minimizing generalization errors
Author(s): Mongkon Eiadon; Luepol Pipanmaekaporn; Suwatchai Kamonsantiroj
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Paper Abstract

Error Correcting Output Code (ECOC) has emerged as one of promising techniques for solving multi-class classification. In the ECOC framework, a multi-class problem is decomposed into several binary ones with a coding design scheme. Despite this, the suitable multi-class decomposition scheme is still ongoing research in machine learning. In this work, we propose a novel multi-class coding design method to mine the effective and compact class codes for multi-class classification. For a given n-class problem, this method decomposes the classes into subsets by embedding a structure of binary trees. We put forward a novel splitting criterion based on minimizing generalization errors across the classes. Then, a greedy search procedure is applied to explore the optimal tree structure for the problem domain. We run experiments on many multi-class UCI datasets. The experimental results show that our proposed method can achieve better classification performance than the common ECOC design methods.

Paper Details

Date Published: 11 July 2016
PDF: 5 pages
Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111D (11 July 2016); doi: 10.1117/12.2242257
Show Author Affiliations
Mongkon Eiadon, King Mongkut's Univ. of North Bangkok (Thailand)
Luepol Pipanmaekaporn, King Mongkut's Univ. of North Bangkok (Thailand)
Suwatchai Kamonsantiroj, King Mongkut's Univ. of North Bangkok (Thailand)


Published in SPIE Proceedings Vol. 10011:
First International Workshop on Pattern Recognition
Xudong Jiang; Guojian Chen; Genci Capi; Chiharu Ishll, Editor(s)

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