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Nonlinear feature selection for support vector quantile regression
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DOI：10.1016/j.neunet.2025.107136.png)
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Abstract
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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
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