Zhongliang Zhang was supported by the National Science Foundation of China (NSFC Proj. 61273204) and CSC Scholarship Program (CSC NO. 201406080059).
Bartosz Krawczyk was supported by the Polish National Science Center under the grant no. UMO-2015/19/B/ST6/01597.
Salvador Garcia and Francisco Herrera were partially supported by the Spanish Ministry of Education and Science under Project TIN2014-57251-P and the Andalusian Research Plan P10-TIC-6858, P11-TIC-7765.
Alejandro Rosales-Perez was supported by the CONACyT grant 329013.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.National Natural Science Foundation of China (NSFC)
61273204CSC Scholarship Program (CSC)
201406080059Polish National Science Center
UMO-2015/19/B/ST6/01597Spanish Government
TIN2014-57251-PAndalusian Research Plan
P10-TIC-6858
P11-TIC-7765Consejo Nacional de Ciencia y Tecnologia (CONACyT)
32901
