In machine learning, fewer features reduce model complexity. Carefully assessing the influence of each input feature on the model quality is therefore a crucial preprocessing step. We propose a novel feature selection algorithm based on a quadratic unconstrained binary optimization (QUBO) problem, which allows to select a specified number of features based on their importance and redundancy. In contrast to iterative or greedy methods, our direct approach yields higherquality solutions. QUBO problems are particularly interesting because they can be solved on quantum hardware. To evaluate our proposed algorithm, we conduct a series of numerical experiments using a classical computer, a quantum gate computer and a quantum annealer. Our evaluation compares our method to a range of standard methods on various benchmark datasets. We observe competitive performance.

翻译：在机器学习中，减少特征数量能够降低模型复杂度。因此，审慎评估每个输入特征对模型质量的影响是一个关键预处理步骤。我们提出了一种基于二次无约束二元优化（QUBO）问题的新型特征选择算法，该算法能够根据特征的重要性和冗余度选择指定数量的特征。与迭代或贪心方法相比，我们的直接方法能够获得更高质量的解决方案。QUBO问题尤其具有吸引力，因为它们可以在量子硬件上求解。为评估所提算法，我们使用经典计算机、量子门计算机和量子退火器开展了一系列数值实验。我们的评估将本方法与多个基准数据集上的多种标准方法进行了比较，观察到其在性能上具有竞争力。