{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T21:55:41Z","timestamp":1774389341672,"version":"3.50.1"},"reference-count":137,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T00:00:00Z","timestamp":1753228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FCT\u2014Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>In the age of rapidly advancing machine learning capabilities, the pursuit of maximum performance encounters the practical limitations imposed by limited resources in several fields. This work presents a cost-effective proposal for feature selection, which is a crucial part of machine learning processes, and intends to partly solve this problem through computational time reduction. The proposed methodology aims to strike a careful balance between feature exploration and strict computational time concerns, by enhancing the quality and relevance of data. This approach focuses on the use of interim representations of feature combinations to significantly speed up a potentially slow and computationally expensive process. This strategy is evaluated in several datasets against other feature selection methods, and the results indicate a significant reduction in the temporal costs associated with this process, achieving a mean percentage decrease of 85%. Furthermore, this reduction is achieved while maintaining competitive model performance, demonstrating that the selected features remain effective for the learning task. These results emphasize the method\u2019s feasibility, confirming its ability to transform machine learning applications in environments with limited resources.<\/jats:p>","DOI":"10.3390\/app15158196","type":"journal-article","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T14:22:44Z","timestamp":1753280564000},"page":"8196","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2665-8057","authenticated-orcid":false,"given":"Duarte","family":"Coelho","sequence":"first","affiliation":[{"name":"ISRC, ISEP, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"},{"name":"Department of Engineering, School of Sciences and Technology, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"E-goi, 4450-190 Matosinhos, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0264-4710","authenticated-orcid":false,"given":"Ana","family":"Madureira","sequence":"additional","affiliation":[{"name":"ISRC, ISEP, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"},{"name":"INESC INOV, 1000-029 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5440-3225","authenticated-orcid":false,"given":"Ivo","family":"Pereira","sequence":"additional","affiliation":[{"name":"ISRC, ISEP, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8698-866X","authenticated-orcid":false,"given":"Ramiro","family":"Gon\u00e7alves","sequence":"additional","affiliation":[{"name":"Department of Engineering, School of Sciences and Technology, Universidade de Tr\u00e1s-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"given":"Susana","family":"Nicola","sequence":"additional","affiliation":[{"name":"ISRC, ISEP, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ci\u00eancia (INESC TEC), Faculdade de Engenharia da Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3915-7421","authenticated-orcid":false,"given":"In\u00eas","family":"C\u00e9sar","sequence":"additional","affiliation":[{"name":"ISRC, ISEP, Polytechnic of Porto, Rua Dr. Ant\u00f3nio Bernardino de Almeida, 4249-015 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8225-5984","authenticated-orcid":false,"given":"Daniel Alves de","family":"Oliveira","sequence":"additional","affiliation":[{"name":"E-goi, 4450-190 Matosinhos, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1328","DOI":"10.1109\/TKDE.2019.2946162","article-title":"A survey on data collection for machine learning: A big data-ai integration perspective","volume":"33","author":"Roh","year":"2019","journal-title":"IEEE Trans. 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