11 July 2016 Mining discriminative class codes for multi-class classification based on minimizing generalization errors
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Proceedings Volume 10011, First International Workshop on Pattern Recognition; 100111D (2016) https://doi.org/10.1117/12.2242257
Event: First International Workshop on Pattern Recognition, 2016, Tokyo, Japan
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.
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Mongkon Eiadon, Mongkon Eiadon, Luepol Pipanmaekaporn, Luepol Pipanmaekaporn, Suwatchai Kamonsantiroj, Suwatchai Kamonsantiroj, } "Mining discriminative class codes for multi-class classification based on minimizing generalization errors", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111D (11 July 2016); doi: 10.1117/12.2242257; https://doi.org/10.1117/12.2242257
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