Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers

• Main Author: Carvalho, Pedro
• Publication Date: 2023
• Other Authors: Ribeiro, Bruno, Rodrigues, Nuno M., Batista, João E., Vanneschi, Leonardo, Silva, Sara
• Language: eng
• Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Download full: http://hdl.handle.net/10362/162072
• topic: Feature Selection; Epistasis; Genetic Algorithms; Genetic Programming; Decision Trees; Machine Learning; Genome-Wide Association Studies; Theoretical Computer Science; Computer Science(all)

Carvalho, P., Ribeiro, B., Rodrigues, N. M., Batista, J. E., Vanneschi, L., & Silva, S. (2023). Feature Selection on Epistatic Problems Using Genetic Algorithms with Nested Classifiers. In J. Correia, S. Smith, & R. Qaddoura (Eds.), Applications of Evolutionary Computation: 26th European Conference, EvoApplications 2023 Held as Part of EvoStar 2023 Brno, Czech Republic, April 12–14, 2023 Proceedings (pp. 656-671). (Lecture Notes in Computer Science; Vol. 13989). Springer. https://doi.org/10.1007/978-3-031-30229-9_42---This work was supported by FCT, Portugal, through funding of LASIGE Research Unit (UIDB/00408/2020, UIDP/00408/2020) and CISUC (UID/CEC/00326/2020); projects AICE (DSAIPA/DS/0113/2019), from FCT, and RETINA (NORTE-01-0145-FEDER-000062), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). The authors acknowledge the work facilities and equipment provided by GECAD research center (UIDB/00760/2020) to the project team. The authors were also supported by their respective PhD grants, Pedro Carvalho (UI/BD/151053/2021), Nuno Rodrigues (2021/05322/BD), João Batista (SFRH/BD/143972/2019).