Nonlinear feature selection for support vector quantile regression

doi:10.1016/j.neunet.2025.107136

Nonlinear feature selection for support vector quantile regression

2025-05-01

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DOI：10.1016/j.neunet.2025.107136

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This paper discusses the nuanced domain of nonlinear feature selection in heterogeneous systems. To address this challenge, we present a sparsity-driven methodology, namely nonlinear feature selection for support vector quantile regression (NFS-SVQR). This method includes a binary-diagonal matrix, featuring 0 and 1 elements, to address the complexities of feature selection within intricate nonlinear systems. Moreover, NFS-SVQR integrates a quantile parameter to effectively address the intrinsic challenges of heterogeneity within nonlinear feature selection processes. Consequently, NFS-SVQR excels not only in precisely identifying representative features but also in comprehensively capturing heterogeneous information within high-dimensional datasets. Through feature selection experiments the enhanced performance of NFS-SVQR in capturing heterogeneous information and selecting representative features is demonstrated.

Keywords:

Sparse learning

Support vector quantile regression

Nonlinear feature selection

Mixed-integer optimization

Nonlinear feature selection for support vector quantile regression

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