In the last decades, a wide portfolio of Feature Weighting (FW) methods have been proposed in the literature. Their main potential is the capability to transform the features in order to contribute to the Machine Learning (ML) algorithm metric proportionally to their estimated relevance for inferring the output pattern. Nevertheless, the extensive number of FW related works makes difficult to do a scientific study in this field of knowledge. Therefore, in this paper a global taxonomy for FW methods is proposed by focusing on: (1) the learning approach (supervised or unsupervised), (2) the methodology used to calculate the weights (global or local), and (3) the feedback obtained from the ML algorithm when estimating the weights (filter or wrapper). Among the different taxonomy levels, an extensive review of the state-of-the-art is presented, followed by some considerations and guide points for the FW strategies selection regarding significant aspects of real-world data analysis problems. Finally, a summary of conclusions and challenges in the FW field is briefly outlined.

在过去的几十年中，文献中提出了广泛的特征加权 (FW) 方法组合。它们的主要潜力是能够转换特征，以便与它们推断输出模式的估计相关性成比例地贡献于机器学习 (ML) 算法指标。尽管如此，大量的 FW 相关作品使得在该知识领域进行科学研究变得困难。因此，本文提出了 FW 方法的全局分类法，重点关注：（1）学习方法（有监督或无监督），（2）用于计算权重（全局或局部）的方法，以及（3）估计权重（过滤器或包装器）时从 ML 算法获得的反馈。在不同的分类级别中，对最新技术进行了广泛的审查，接下来是关于现实世界数据分析问题的重要方面的 FW 策略选择的一些考虑因素和指导点。最后，简要概述了 FW 领域的结论和挑战。