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2017 | OriginalPaper | Chapter

Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems

Authors : Christos K. Aridas, Stamatios-Aggelos N. Alexandropoulos, Sotiris B. Kotsiantis, Michael N. Vrahatis

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

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Abstract

One of the most common approaches for handling the multi-class classification problem is to divise the original data set into binary subclasses and to use a set of binary classifiers in order to solve the binarization problem. A new method for solving multi-class classification problems is proposed, by incorporating random resampling techniques in the one-versus-all strategy. Specifically, the division used by the proposed method is based on the one-versus-all binarization technique using random resampling for handling the class-imbalance problem arising due to the one-versus-all binarization. The method has been tested extensively on several multiclass classification problems using Support Vector Machines with four different kernels. Experimental results show that the proposed method exhibits a better performance compared to the simple one-versus-all.

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Metadata
Title
Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems
Authors
Christos K. Aridas
Stamatios-Aggelos N. Alexandropoulos
Sotiris B. Kotsiantis
Michael N. Vrahatis
Copyright Year
2017
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-319-65171-2

Electronic ISBN: 978-3-319-65172-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-65172-9_10

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