This paper proposes a novel supervised feature selection method named NeuroFS. NeuroFS introduces dynamic neuron evolution in the training process of a sparse neural network to find an informative set of features. By evaluating NeuroFS on real-world benchmark datasets, we demonstrated that it achieves the highest ranking-based score among the considered state-of-the-art supervised feature selection models. However, due to the general lack of knowledge on optimally implementing sparse neural networks during training, NeuroFS does not take full advantage of its theoretical high computational and memory advantages. We let the development of this challenging research direction for future work, hopefully, in a greater joint effort of the community.

翻译：本文提出了一种名为NeuroFS的新型监督特征选择方法。NeuroFS通过在稀疏神经网络训练过程中引入动态神经元演化机制，以寻找信息丰富的特征子集。通过在真实世界基准数据集上的评估，我们证明了该方法在现有最先进的监督特征选择模型中获得了最高的排序得分。然而，由于在训练过程中对稀疏神经网络的最佳实现方式普遍缺乏认知，NeuroFS未能充分发挥其理论上的高计算效率与内存优势。我们将这一具有挑战性的研究方向留待未来工作中解决，期望学界能携手共进。