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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Acknowledgements
Stamatios-Aggelos N. Alexandropoulos gratefully acknowledges the support of his work by the Hellenic State Scholarships Foundation (IKY), co-financed by the European Union (European Social Fund–ESF) and Greek national funds, “Reinforcement of the Human Research Potential through Doctoral Research” of the Operational Program “Development of Human Capital, Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF 2014–2020).
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Aridas, C.K., Alexandropoulos, SA.N., Kotsiantis, S.B., Vrahatis, M.N. (2017). Random Resampling in the One-Versus-All Strategy for Handling Multi-class Problems. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_10
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