Optimised Feature Subset Selection via Simulated Annealing

• URL: http://arxiv.org/abs/2507.23568v1
• Date: Thu, 31 Jul 2025 13:57:38 GMT
• Title: Optimised Feature Subset Selection via Simulated Annealing
• Authors: Fernando Martínez-García, Álvaro Rubio-García, Samuel Fernández-Lorenzo, Juan José García-Ripoll, Diego Porras,
• Abstract summary: We introduce SA-FDR, a novel algorithm for $ell_0$-norm feature selection.<n>We show that SA-FDR consistently selects more compact feature subsets while achieving a high predictive accuracy.<n>As a result, SA-FDR provides a flexible and effective solution for designing interpretable models in high-dimensional settings.
• Score: 39.58317527488534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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