用模拟退火找最优特征子集，又准又简洁。
Optimised Feature Subset Selection via Simulated Annealing
- 把选特征当组合优化问题，用模拟退火全局搜索
- 在数十万样本数据上，特征更少且准确率高
- 适合需要模型简洁、可解释的高维数据场景
我们提出SA-FDR算法，将ℓ₀范数特征选择建模为组合优化问题，采用模拟退火在特征子集空间中进行全局搜索。优化过程以费希尔判别比为代理指标，高效评估分类任务中的模型质量。实验在含数十万样本和数百特征的数据集上进行，结果表明SA-FDR能持续选出更紧凑的特征子集，同时保持高预测准确率。该方法能捕捉贪婪算法常忽略的特征间依赖关系，从而在高维场景下实现更强的稀疏性与可解释性，特别适用于对模型简洁性、可解释性与性能均敏感的任务。
原文摘要 · Abstract (English)
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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