Castillo-García, G.Morán-Fernández, LauraBolón-Canedo, Verónica2026-04-152026-04-152023G. Castillo-García, L. Morán-Fernández, and V. Bolón-Canedo, "Efficient feature selection for domain adaptation using Mutual Information Maximization", ESANN 2023 Proceedings - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2023, p. 285-290, https://doi.org/10.14428/esann/2023.ES2023-61978-2-87587-088-9https://hdl.handle.net/2183/47993Presented at: ESANN 2023 - 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges, Belgium, 04 - 06 October, 2023[Abstract]: Green AI, an emerging research field, focuses on improving the efficiency of machine learning models. In this paper, we introduce a novel and efficient method for feature selection in domain adaptation, a type of transfer learning where the source and target domains share the feature space and task but differ in their distributions. Instead of using evolutionary algorithms, a typical approach in this field, we propose the use of filter methods, which do not require an iterative search process and are less computationally expensive. Our proposed method is Mutual Information Maximization, and our experiments show that it outperforms Particle Swarm Optimization in terms of efficiency, speed, and the ability to select a reduced subset of features while achieving competitive classification accuracy results.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.Domain AdaptationMutual InformationFeature Selectionconference outputopen access10.14428/esann/2023.ES2023-61