Handling Imbalanced Classification Problems with Support Vector Machines via Evolutionary Bilevel Optimization
Identificadores
URI: http://hdl.handle.net/10481/74696Metadatos
Afficher la notice complèteEditorial
IEEE
Materia
Support Vector Machines Imbalanced classification Data Preprocessing Evolutionary algorithms Bilevel optimization
Date
2022-04-21Referencia bibliográfica
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]
Patrocinador
PID2020-119478GBI00 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 CONACyTRésumé
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.