Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications
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 proce...
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| Format: | Article |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8196 |
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| author | Duarte Coelho Ana Madureira Ivo Pereira Ramiro Gonçalves Susana Nicola Inês César Daniel Alves de Oliveira |
| author_facet | Duarte Coelho Ana Madureira Ivo Pereira Ramiro Gonçalves Susana Nicola Inês César Daniel Alves de Oliveira |
| author_sort | Duarte Coelho |
| collection | DOAJ |
| description | 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’s feasibility, confirming its ability to transform machine learning applications in environments with limited resources. |
| format | Article |
| id | doaj-art-d2ed8ba0876e4d5aab935c0015a371e1 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d2ed8ba0876e4d5aab935c0015a371e12025-08-20T04:00:54ZengMDPI AGApplied Sciences2076-34172025-07-011515819610.3390/app15158196Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning ApplicationsDuarte Coelho0Ana Madureira1Ivo Pereira2Ramiro Gonçalves3Susana Nicola4Inês César5Daniel Alves de Oliveira6ISRC, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalISRC, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalISRC, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalDepartment of Engineering, School of Sciences and Technology, Universidade de Trás-os-Montes e Alto Douro, 5000-801 Vila Real, PortugalISRC, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalISRC, ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, PortugalE-goi, 4450-190 Matosinhos, PortugalIn 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’s feasibility, confirming its ability to transform machine learning applications in environments with limited resources.https://www.mdpi.com/2076-3417/15/15/8196dimensionality reductionfeature engineeringfeature selectionmachine learning |
| spellingShingle | Duarte Coelho Ana Madureira Ivo Pereira Ramiro Gonçalves Susana Nicola Inês César Daniel Alves de Oliveira Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications Applied Sciences dimensionality reduction feature engineering feature selection machine learning |
| title | Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications |
| title_full | Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications |
| title_fullStr | Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications |
| title_full_unstemmed | Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications |
| title_short | Leveraging Feature Extraction to Perform Time-Efficient Selection for Machine Learning Applications |
| title_sort | leveraging feature extraction to perform time efficient selection for machine learning applications |
| topic | dimensionality reduction feature engineering feature selection machine learning |
| url | https://www.mdpi.com/2076-3417/15/15/8196 |
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