RT Journal Article
T1 Robust SVM Classification with l0-Norm Feature Selection
A1 Carrasco, Miguel
A1 Ivorra, Benjamín Pierre Paul
A1 López, Julio
A1 Ramos Del Olmo, Ángel Manuel
AB In this article, we introduce a robust classification model designed for feature selection. Support vector machine (SVM) models continue to play a crucial role in binary classification, particularly with tabular data. Their robust variants are essential for developing classifiers that remain stable despite shifts in data distribution. Additionally, sparse classifiers are highly desirable, as they offer improved performance in classification tasks and help reduce overfitting, especially when the number of features exceeds the number of samples.  In this context, penalty methods for feature selection are fundamental to the development of sparse optimization models. Despite its inherent nonlinearity and nonconvexity, the l0-norm remains a popular choice for this task and has been effectively  applied in diverse fields, such as pattern recognition and signal processing. Our objective is to evaluate the performance of two classification approaches that integrate robust classification in SVM-type models with embedded feature selection. We propose a diagonal algorithm for numerically solving these optimization models, which we validate through numerical tests on benchmark datasets. The results are compared to those obtained using models based on l2-norm regularization.
YR 2024
FD 2024
LK https://hdl.handle.net/20.500.14352/109030
UL https://hdl.handle.net/20.500.14352/109030
LA eng
NO Ministry of Science and Innovation (Spain)
DS Docta Complutense
RD 7 abr 2025