Boosting any learning algorithm with Statistically Enhanced Learning
Abstract Feature engineering is of critical importance in the field of Data Science. While any data scientist knows the importance of rigorously preparing data to obtain good performing models, only scarce literature formalizes its benefits. In this work, we present the method of Statistically Enhan...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-84702-8 |
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| author | Florian Felice Christophe Ley Stéphane P. A. Bordas Andreas Groll |
| author_facet | Florian Felice Christophe Ley Stéphane P. A. Bordas Andreas Groll |
| author_sort | Florian Felice |
| collection | DOAJ |
| description | Abstract Feature engineering is of critical importance in the field of Data Science. While any data scientist knows the importance of rigorously preparing data to obtain good performing models, only scarce literature formalizes its benefits. In this work, we present the method of Statistically Enhanced Learning (SEL), a formalization framework of existing feature engineering and extraction tasks in Machine Learning (ML). Contrary to existing approaches, predictors are not directly observed but obtained as statistical estimators. Our goal is to study SEL, aiming to establish a formalized framework and illustrate its improved performance by means of simulations as well as applications on practical use cases. |
| format | Article |
| id | doaj-art-13ebdc0aee8c4b6cb00c565e70cf795a |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-13ebdc0aee8c4b6cb00c565e70cf795a2025-01-12T12:16:03ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-84702-8Boosting any learning algorithm with Statistically Enhanced LearningFlorian Felice0Christophe Ley1Stéphane P. A. Bordas2Andreas Groll3Department of Mathematics, University of LuxembourgDepartment of Mathematics, University of LuxembourgDepartment of Engineering, University of LuxembourgDepartment of Statistics, University of DortmundAbstract Feature engineering is of critical importance in the field of Data Science. While any data scientist knows the importance of rigorously preparing data to obtain good performing models, only scarce literature formalizes its benefits. In this work, we present the method of Statistically Enhanced Learning (SEL), a formalization framework of existing feature engineering and extraction tasks in Machine Learning (ML). Contrary to existing approaches, predictors are not directly observed but obtained as statistical estimators. Our goal is to study SEL, aiming to establish a formalized framework and illustrate its improved performance by means of simulations as well as applications on practical use cases.https://doi.org/10.1038/s41598-024-84702-8Feature extractionMachine learningStatistics |
| spellingShingle | Florian Felice Christophe Ley Stéphane P. A. Bordas Andreas Groll Boosting any learning algorithm with Statistically Enhanced Learning Scientific Reports Feature extraction Machine learning Statistics |
| title | Boosting any learning algorithm with Statistically Enhanced Learning |
| title_full | Boosting any learning algorithm with Statistically Enhanced Learning |
| title_fullStr | Boosting any learning algorithm with Statistically Enhanced Learning |
| title_full_unstemmed | Boosting any learning algorithm with Statistically Enhanced Learning |
| title_short | Boosting any learning algorithm with Statistically Enhanced Learning |
| title_sort | boosting any learning algorithm with statistically enhanced learning |
| topic | Feature extraction Machine learning Statistics |
| url | https://doi.org/10.1038/s41598-024-84702-8 |
| work_keys_str_mv | AT florianfelice boostinganylearningalgorithmwithstatisticallyenhancedlearning AT christopheley boostinganylearningalgorithmwithstatisticallyenhancedlearning AT stephanepabordas boostinganylearningalgorithmwithstatisticallyenhancedlearning AT andreasgroll boostinganylearningalgorithmwithstatisticallyenhancedlearning |