Suárez-Marcote, SamuelMorán-Fernández, LauraBolón-Canedo, Verónica2026-04-152026-04-152023S. Suárez-Marcote, L. Morán-Fernández, and V. Bolón-Canedo, "Logarithmic division for green feature selection: an information-theoretic approach", ESANN 2023 Proceedings - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2023, p. 279-284, https://doi.org/10.14428/esann/2023.ES2023-77978-2-87587-088-9https://hdl.handle.net/2183/47992Presented at: ESANN 2023 - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 04 - 06 October, 2023[Abstract]: Feature selection is a popular preprocessing step to reduce the dimensionality of the data while preserving the important information. In this paper we propose an efficient and green feature selection method based on information theory, with the novelty of using the logarithmic division and resort to fixed-point precision. The results of experiments conducted on several datasets indicate the potential of our proposal, as it does not incur in significant information loss compared to the standard method, both in the features selected and in the subsequent classification step. This finding opens up possibilities for a new family of green feature selection methods, which would help to minimize energy consumption and carbon emissions.eng© ESANN 2023. All rights reserved. This is the published version of the paper, distributed in accordance with ESANN's self-archiving policy, which allows authors to archive their work in any repository provided that full reference is made to the ESANN publication.Feature selectionInformation theoryGreen computingconference outputopen access10.14428/esann/2023.ES2023-77