An efficiency curve for evaluating imbalanced classifiers considering intrinsic data characteristics: Experimental analysis Chao, Xiangrui Fernández Hilario, Alberto Luis Classification Imbalanced dataset Data intrinsic characteristics Assessment metrics Efficiency Balancing the accuracy rates of the majority and minority classes is challenging in imbalanced classification. Furthermore, data characteristics have a significant impact on the performance of imbalanced classifiers, which are generally neglected by existing evaluation methods. The objective of this study is to introduce a new criterion to comprehensively evaluate imbalanced classifiers. Specifically, we introduce an efficiency curve that is established using data envelopment analysis without explicit inputs (DEA-WEI), to determine the trade-off between the benefits of improved minority class accuracy and the cost of reduced majority class accuracy. In sequence, we analyze the impact of the imbalanced ratio and typical imbalanced data characteristics on the efficiency of the classifiers. Empirical analyses using 68 imbalanced data reveal that traditional classifiers such as C4.5 and the k-nearest neighbor are more effective on disjunct data, whereas ensemble and undersampling techniques are more effective for overlapping and noisy data. The efficiency of cost-sensitive classifiers decreases dramatically when the imbalanced ratio increases. Finally, we investigate the reasons for the different efficiencies of classifiers on imbalanced data and recommend steps to select appropriate classifiers for imbalanced data based on data characteristics. 2022-09-27T12:36:09Z 2022-09-27T12:36:09Z 2022-06-22 journal article Xiangrui Chao... [et al.]. An efficiency curve for evaluating imbalanced classifiers considering intrinsic data characteristics: Experimental analysis, Information Sciences, Volume 608, 2022, Pages 1131-1156, ISSN 0020-0255, [https://doi.org/10.1016/j.ins.2022.06.045] https://hdl.handle.net/10481/77017 10.1016/j.ins.2022.06.045 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier