Weight-based feature selection (WBFS) are methods used to measure the contribution of input to output in a trained artificial neural network (ANN). Furthermore, algorithms such as Garson’s rely upon a single best neural network model or the mean importance value of several ANNs. However, different initialization weights lead to different importance values, as reported in other studies. These differences are misleading since each rank could result in different scores, altering the position of a variable in a given rank. Therefore, we propose a new methodology to assess the stability of a WBFS method. In essence, the idea is to use a voting approach to evaluate the importance of rankings. The results showed that Garson’s, Olden’s and Yoon’s algorithms are more stable methods when applied to artificial datasets. Nevertheless, its stability is considerably reduced when applied to real-world datasets. Hence, we concluded that future work should take into consideration the aforementioned instability of existing WBFS methods as applied to complex real-world data.

基于权重的特征选择（WBFS）是用于在经过训练的人工神经网络（ANN）中测量输入对输出贡献的方法。此外，诸如Garson的算法依赖于单个最佳神经网络模型或多个ANN的平均重要性值。但是，如其他研究报告中所述，不同的初始化权重会导致不同的重要性值。这些差异具有误导性，因为每个等级可能导致不同的得分，从而改变变量在给定等级中的位置。因此，我们提出了一种新的方法来评估WBFS方法的稳定性。本质上，该想法是使用投票方法来评估排名的重要性。结果表明，当应用于人工数据集时，Garson，Olden和Yoon算法是更稳定的方法。不过，当将其应用于实际数据集时，其稳定性会大大降低。因此，我们得出结论，未来的工作应考虑到现有的WBFS方法应用于复杂的现实世界数据时的上述不稳定性。