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2018 | OriginalPaper | Buchkapitel

Multi-class Imbalanced Learning with One-Versus-One Decomposition: An Empirical Study

verfasst von : Yanjun Song, Jing Zhang, Han Yan, Qianmu Li

Erschienen in: Cloud Computing and Security

Verlag: Springer International Publishing

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Abstract

In supervised learning, the underlying skewed distribution of multiple classes poses extreme difficulties for learning good models. A common scheme to deal with the multi-class imbalanced problem is to decompose an original dataset into several binary-class subsets and incorporate some imbalanced learning techniques. This paper presents our empirical study on the state-of-the-art multi-class imbalanced learning algorithms which are based on One-versus-One (OVO) decomposition. We implemented six algorithms in literature, including SMOTEBagging, UnderBagging, OVO plus OVA, OVO plus SMOTE, One-Against-Higher-Order, and DynamicOVO, and evaluate their performance in terms of multi-class Area Under the ROC (MAUC) on eighteen datasets with different characteristics. Experimental results show that the OVO plus SMOTE algorithm is superior to other algorithms and it is quite stable.

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Titel: Multi-class Imbalanced Learning with One-Versus-One Decomposition: An Empirical Study
verfasst von: Yanjun Song
Jing Zhang
Han Yan
Qianmu Li
Verlag: Springer International Publishing
Buch: Cloud Computing and Security
Print ISBN: 978-3-030-00011-0

Electronic ISBN: 978-3-030-00012-7

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-00012-7_56

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