In this paper, we study the effect that not distinguishing between absent and non-observed features may have on the subset of features that is selected upon ...
In this paper, we study the effect that not distinguishing between absent and non-observed features may have on the subset of features that is selected upon ...
Oct 22, 2024 · In this paper, we study the effect that not distinguishing between absent and non-observed features may have on the subset of features that is ...
On the robustness of feature selection with absent and non-observed features ; J.M. Barreiro, F. Martin-Sanchez, V. Maojo, F. Sanz · Heidelberg · Springer · 148-159.
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In this paper, we investigate the adversarial robustness of feature selection based on the ℓ 1 regularized linear regression method, named LASSO.
Sep 1, 2022 · We propose a novel robust unsupervised feature selection method, called Unsupervised Feature Selection with Robust Data Reconstruction (UFS-RDR),
Jointly analyzing transcriptomic data and the existing biological networks can yield more robust and informative feature selection results, ...
Jul 7, 2023 · Robust feature selection is vital for creating reliable and interpretable machine-learning (ML) models.
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Nov 11, 2024 · Feature selection plays a critical role in reducing the dimensionality of input data, thereby improving machine learning efficiency and ...