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:这份论文提出了名为NeuroFS的新颖的受监督地物选择方法。NeuroFS引入了稀有神经网络培训过程中的动态神经进化,以寻找一套内容丰富的特征。通过对现实世界基准数据集的NeuroFS进行评估,我们证明,它在被视为最先进的受监督地物选择模型中取得了最高的排名分数。然而,由于在培训期间普遍缺乏关于最佳实施稀有神经网络的知识,NeuroFS没有充分利用其理论高计算和记忆优势。我们希望,在社区更大程度的共同努力下,为未来工作制定这一具有挑战性的研究方向。</s>