# Optimised Feature Subset Selection via Simulated Annealing

arXiv: https://arxiv.org/abs/2507.23568 · HTML: https://mlsanity.com/paper/2507.23568/

## Key facts

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- First posted on arXiv 31 Jul 2025.

## Abstract (arXiv)

We introduce SA-FDR, a novel algorithm for ℓ0-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.