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N-ary decomposition for multi-class classification

Authors
Zhou, Joey TianyiTsang, Ivor W.Ho, Shen-ShyangMueller, Klaus-Robert
Issue Date
5월-2019
Publisher
SPRINGER
Keywords
Ensemble learning; Multi-class classification; N-ary ECOC
Citation
MACHINE LEARNING, v.108, no.5, pp.809 - 830
Indexed
SCIE
SCOPUS
Journal Title
MACHINE LEARNING
Volume
108
Number
5
Start Page
809
End Page
830
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/65874
DOI
10.1007/s10994-019-05786-2
ISSN
0885-6125
Abstract
A common way of solving a multi-class classification problem is to decompose it into a collection of simpler two-class problems. One major disadvantage is that with such a binary decomposition scheme it may be difficult to represent subtle between-class differences in many-class classification problems due to limited choices of binary-value partitions. To overcome this challenge, we propose a new decomposition method called N-ary decomposition that decomposes the original multi-class problem into a set of simpler multi-class subproblems. We theoretically show that the proposed N-ary decomposition could be unified into the framework of error correcting output codes and give the generalization error bound of an N-ary decomposition for multi-class classification. Extensive experimental results demonstrate the state-of-the-art performance of our approach.
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