Handling Imbalanced Classification Problems with Support Vector Machines via Evolutionary Bilevel Optimization Rosales Pérez, Alejandro García López, Salvador Herrera Triguero, Francisco Support Vector Machines Imbalanced classification Data Preprocessing Evolutionary algorithms Bilevel optimization This work was partially supported by Project PID2020-119478GBI00 funded by MICINN/AEI/10.13039/501100011033 and by Project A-TIC-434-UGR20 funded by FEDER/Junta de Andaluc´ıa- Consejer´ıa de Transformaci´on Econ´omica, Industria, Conocimiento y Universidades. The authors acknowledge the support from “Laboratorio de Superc´omputo del Baj´ıo” through project 300832 from CONACyT. Support vector machines are popular learning algorithms to deal with binary classification problems. They traditionally assume equal misclassification costs for each class; however, real-world problems may have an uneven class distribution. This paper introduces EBCS-SVM: Evolutionary Bilevel Costsensitive Support Vector Machines. EBCS-SVM handles imbalanced classification problems by simultaneously learning the support vectors and optimizing the SVM hyper-parameters, which comprise the kernel parameter and misclassification costs. The resulting optimization problem is a bilevel problem, where the lower-level determines the support vectors and the upper-level the hyper-parameters. This optimization problem is solved using an evolutionary algorithm at the upper-level and Sequential Minimal Optimization at the lower-level. These two methods work in a nested fashion, i.e., the optimal support vectors help guide the search of the hyper-parameters, and the lower-level is initialized based on previous successful solutions. The proposed method is assessed using 70 datasets of imbalanced classification and compared with several state-of-the-art methods. The experimental results, supported by a Bayesian test, provided evidence of the effectiveness of EBCS-SVM when working with highly imbalanced datasets. 2022-05-04T11:41:55Z 2022-05-04T11:41:55Z 2022-04-21 info:eu-repo/semantics/article Published version: A. Rosales-Pérez, S. García and F. Herrera, "Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization," in IEEE Transactions on Cybernetics, doi: [10.1109/TCYB.2022.3163974] http://hdl.handle.net/10481/74696 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España IEEE