Abstract
Feature Selection for mixed data is an active research area with many applications in practical problems where numerical and non-numerical features describe the objects of study. This paper provides the first comprehensive and structured revision of the existing supervised and unsupervised feature selection methods for mixed data reported in the literature. Additionally, we present an analysis of the main characteristics, advantages, and disadvantages of the feature selection methods reviewed in this survey and discuss some important open challenges and potential future research opportunities in this field.
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Notes
Also called heterogeneous or assorted data.
The label assigned to each object in the dataset can be a category, an ordered value, or a real value, depending on the specific task.
For the case of UFS methods, class labels are not used in this step.
A parameter given by the user in the range (0, 1) that specifies the average fraction of features per cluster.
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The first author gratefully acknowledges to the Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) for the collaboration grant awarded for the completion of this survey.
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Solorio-Fernández, S., Carrasco-Ochoa, J. & Martínez-Trinidad, J.F. A survey on feature selection methods for mixed data. Artif Intell Rev 55, 2821–2846 (2022). https://doi.org/10.1007/s10462-021-10072-6
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DOI: https://doi.org/10.1007/s10462-021-10072-6