@misc{10481/65075,
year = {2016},
month = {4},
url = {http://hdl.handle.net/10481/65075},
abstract = {Multi-class imbalance classification problems occur in many real-world applications, which suffer from the quite different distribution of classes. Decomposition strategies are well-known techniques to address the classification problems involving multiple classes. Among them binary approaches using one-vs-one and one-vs-all has gained a significant attention from the research community. They allow to divide multi-class problems into several easier-to-solve two-class sub-problems. In this study we develop an exhaustive empirical analysis to explore the possibility of empowering the one-vs-one scheme for multi-class imbalance classification problems with applying binary ensemble learning approaches. We examine several state-of-the-art ensemble learning methods proposed for addressing the imbalance problems to solve the pairwise tasks derived from the multi-class data set. Then the aggregation strategy is employed to combine the binary ensemble outputs to reconstruct the original multi-class task. We present a detailed experimental study of the proposed approach, supported by the statistical analysis. The results indicate the high effectiveness of ensemble learning with one-vs-one scheme in dealing with the multi-class imbalance classification problems.},
organization = {National Natural Science Foundation of China (NSFC)
	
61273204},
organization = {CSC Scholarship Program (CSC) 	
201406080059},
organization = {Polish National Science Center 	
UMO-2015/19/B/ST6/01597},
organization = {Spanish Government
	
TIN2014-57251-P},
organization = {Andalusian Research Plan 	
P10-TIC-6858
P11-TIC-7765},
organization = {Consejo Nacional de Ciencia y Tecnologia (CONACyT)
	
329013},
publisher = {Elsevier},
keywords = {Multi-class classification},
keywords = {Imbalanced data},
keywords = {Ensemble learning},
keywords = {Binary decomposition},
keywords = {Classifier combination},
title = {Empowering One-vs-One Decomposition with Ensemble Learning for Multi-Class Imbalanced Data},
doi = {10.1016/j.knosys.2016.05.048},
author = {Zhang, Zhongliang and Krawczyk, Bartosz and García López, Salvador and Rosales-Pérez, Alejandro and Herrera Triguero, Francisco},
}