Assessment of the influence of features on a classification problem: an application to COVID-19 patients
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Elsevier
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This paper deals with an important subject in classification problems addressed by machine learning techniques: the evaluation of the influence of each of the features on the classification of individuals. Specifically, a measure of that influence is introduced using the Shapley value of cooperative games. In addition, an axiomatic characterisation of the proposed measure is provided based on properties of efficiency and balanced contributions. Furthermore, some experiments have been designed in order to validate the appropriate performance of such measure. Finally, the methodology introduced is applied to a sample of COVID-19 patients to study the influence of certain demographic or risk factors on various events of interest related to the evolution of the disease
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European Journal of Operational Research 299 (2022) 631-641. https://doi.org/10.1016/j.ejor.2021.09.027
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https://doi.org/10.1016/j.ejor.2021.09.027Sponsors
The authors are grateful to Ricardo Cao Abad and to the Dirección Xeral de Saúde Pública of the Xunta de Galicia in Spain. This work has been supported by the ERDF, the Government of Spain/AEI [grants MTM2017-87197-C3-1-P and MTM2017-87197-C3-3-P]; the Xunta de Galicia [Grupos de Referencia Competitiva ED431C2016-015, ED431C2017/38, and ED431C 2021/24, and Centro Singular de Investigación de Galicia ED431G/01]; and by the collaborative research project of the IMAT “Mathematical, statistical and dynamic study of the epidemic COVID-19”, subsidized by the Vice-Rector’s Office for Research and Innovation at the University of Santiago de Compostela, Spain. The research of Laura Davila-Pena has been funded by the Government of Spain [grant FPU17/02126]. We would also like to thank the three anonymous referees and the editor for their constructive comments and suggestions, which helped us to improve the final version of this paper
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© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)








