Abstract
This paper introduces the application of feature ranking with a twofold purpose: first it achieves a feature sorting which becomes into a feature selection procedure given that a threshold is defined to only retain features with a positive influence with the class label (feature sorting and feature selection: FSoFS), second the feature subset is optimised using feature ranking to get a promising order of attributes (FSo). The supporting paper introduced a single stage: Feature ranking for feature sorting and feature selection, FR4(FS)\(^2\) with the shortcoming that for some high-dimensional classification problems the pre-processed data set did not obtain a more accurate classification model than the raw classifier. The results mean that it deserves to consider feature ranking in a couple of sequential stages with different goals: i) feature sorting, accompanied by feature selection, and ii) also alone
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This work has been partially subsidised by the project US-1263341 (Junta de Andalucía) and FEDER funds.
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Tallón-Ballesteros, A.J., Márquez-Rodríguez, A., Wu, Y., Santana-Morales, P., Fong, S. (2023). Feature Ranking for Feature Sorting and Feature Selection, and Feature Sorting: FR4(FSoFS)\(\wedge \)FSo. In: García Bringas, P., et al. 17th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2022). SOCO 2022. Lecture Notes in Networks and Systems, vol 531. Springer, Cham. https://doi.org/10.1007/978-3-031-18050-7_56
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