Mining discriminative class codes for multi-class classification based on minimizing generalization errors

Mongkon Eiadon; Luepol Pipanmaekaporn; Suwatchai Kamonsantiroj

doi:10.1117/12.2242257

11 July 2016 Mining discriminative class codes for multi-class classification based on minimizing generalization errors

Mongkon Eiadon, Luepol Pipanmaekaporn, Suwatchai Kamonsantiroj

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.

Citation Download Citation

Mongkon Eiadon, Luepol Pipanmaekaporn, and 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); https://doi.org/10.1117/12.2242257

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