Article
N-ary decomposition for multi-class classification
Machine Learning
(2019)
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.
Disciplines
Publication Date
May 1, 2019
DOI
10.1007/s10994-019-05786-2
Citation Information
Joey Tianyi Zhou, Ivor W. Tsang, Shen Shyang Ho and Klaus Robert Müller. "N-ary decomposition for multi-class classification" Machine Learning Vol. 108 Iss. 5 (2019) p. 809 - 830 Available at: http://works.bepress.com/shen-shyang-ho/11/