Feature weighting methods: A review. (1st December 2021) - Niño-Adan, Iratxe; Manjarres, Diana; Landa-Torres, Itziar; Portillo, Eva
Abstract: 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. Highlights: An extensive review of Feature Weighting methods based on a new global taxonomy. A new global taxonomy focused on the learning strategy, methodology and feedback. A classification based on the input data, application and ML performance measure. Considerations and guide points for selecting the FW strategy. A collection of unsolved challenges for future work in this scientific field.