Optimised Feature Subset Selection via Simulated Annealing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913968365240320 |
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| author | Martínez-García, Fernando Rubio-García, Álvaro Fernández-Lorenzo, Samuel García-Ripoll, Juan José Porras, Diego |
| author_facet | Martínez-García, Fernando Rubio-García, Álvaro Fernández-Lorenzo, Samuel García-Ripoll, Juan José Porras, Diego |
| contents | We introduce SA-FDR, a novel algorithm for $\ell_0$-norm feature selection that considers this task as a combinatorial optimisation problem and solves it by using simulated annealing to perform a global search over the space of feature subsets. The optimisation is guided by the Fisher discriminant ratio, which we use as a computationally efficient proxy for model quality in classification tasks. Our experiments, conducted on datasets with up to hundreds of thousands of samples and hundreds of features, demonstrate that SA-FDR consistently selects more compact feature subsets while achieving a high predictive accuracy. This ability to recover informative yet minimal sets of features stems from its capacity to capture inter-feature dependencies often missed by greedy optimisation approaches. As a result, SA-FDR provides a flexible and effective solution for designing interpretable models in high-dimensional settings, particularly when model sparsity, interpretability, and performance are crucial. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_23568 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Optimised Feature Subset Selection via Simulated Annealing Martínez-García, Fernando Rubio-García, Álvaro Fernández-Lorenzo, Samuel García-Ripoll, Juan José Porras, Diego Machine Learning Statistical Mechanics We introduce SA-FDR, a novel algorithm for $\ell_0$-norm feature selection that considers this task as a combinatorial optimisation problem and solves it by using simulated annealing to perform a global search over the space of feature subsets. The optimisation is guided by the Fisher discriminant ratio, which we use as a computationally efficient proxy for model quality in classification tasks. Our experiments, conducted on datasets with up to hundreds of thousands of samples and hundreds of features, demonstrate that SA-FDR consistently selects more compact feature subsets while achieving a high predictive accuracy. This ability to recover informative yet minimal sets of features stems from its capacity to capture inter-feature dependencies often missed by greedy optimisation approaches. As a result, SA-FDR provides a flexible and effective solution for designing interpretable models in high-dimensional settings, particularly when model sparsity, interpretability, and performance are crucial. |
| title | Optimised Feature Subset Selection via Simulated Annealing |
| topic | Machine Learning Statistical Mechanics |
| url | https://arxiv.org/abs/2507.23568 |