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This paper addresses the challenge of high-dimensional feature selection in molecular biology datasets by improving upon Independent Dominating Set models through a node packing formulation. The proposed method enhances predictive accuracy and stability over traditional approaches while explicitly managing feature correlations and diversity. It represents a significant advancement in combinatorial optimization techniques applied to machine learning workflows for omics data analysis.
Pack and Extract: An Effective Graph-Based Approach for Feature Selection
arXiv:2610.23938v1 Announce Type: new Abstract: Many molecular biology data sets contain thousands of features, such as genes in single-cell experiments or other omics data. Standard predictive models often perform poorly in such settings, as they tend to retain a large number of features and fail to adequately handle correlations among them. Combinatorial approaches, such as feature selection based on the Independent Dominating Set (IDS) model, have shown promising results, but appear to have been largely overlooked, possibly due to their high computational complexity. In this paper, we propose an improvement to IDS-based feature selection that (i) favors features exhibiting greater diversity and (ii) generalizes the notion of independent dominating set to a node packing model. The resulting formulation is used as an intermediate step within the feature selection process. We evaluate our approach on five genetic data sets and compare the results with those obtained using existing methods. Our experimental results demonstrate notable improvements in predictive accuracy over traditional models, as well as increased stability of the selected feature sets.
Abstract:Many molecular biology data sets contain thousands of features, such as genes in single-cell experiments or other omics data. Standard predictive models often perform poorly in such settings, as they tend to retain a large number of features and fail to adequately handle correlations among them. Combinatorial approaches, such as feature selection based on the Independent Dominating Set (IDS) model, have shown promising results, but appear to have been largely overlooked, possibly due to their high computational complexity. In this paper, we propose an improvement to IDS-based feature selection that (i) favors features exhibiting greater diversity and (ii) generalizes the notion of independent dominating set to a node packing model. The resulting formulation is used as an intermediate step within the feature selection process. We evaluate our approach on five genetic data sets and compare the results with those obtained using existing methods. Our experimental results demonstrate notable improvements in predictive accuracy over traditional models, as well as increased stability of the selected feature sets.
Machine Learning (cs.LG)
arXiv:2610.23938 [cs.LG]
(or arXiv:2610.23938v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.23938 Focus to learn more arXiv-issued DOI via DataCite
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