Handling Imbalanced Classification Problems with Support Vector Machines via Evolutionary Bilevel Optimization
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Support Vector MachinesImbalanced classificationData PreprocessingEvolutionary algorithmsBilevel optimization
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]
SponsorshipPID2020-119478GBI00 funded by MICINN/AEI/10.13039/501100011033; A-TIC-434-UGR20 funded by FEDER/Junta de Andaluc´ıa- Consejer´ıa de Transformaci´on Econ´omica, Industria, Conocimiento y Universidades; 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.