Machine Learning Training Optimization using the Barycentric Correction Procedure
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913250323464192 |
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| author | Ramos-Pulido, Sofia Hernandez-Gress, Neil Ceballos-Cancino, Hector G. |
| author_facet | Ramos-Pulido, Sofia Hernandez-Gress, Neil Ceballos-Cancino, Hector G. |
| contents | Machine learning (ML) algorithms are predictively competitive algorithms with many human-impact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_00542 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Training Optimization using the Barycentric Correction Procedure Ramos-Pulido, Sofia Hernandez-Gress, Neil Ceballos-Cancino, Hector G. Machine Learning Machine learning (ML) algorithms are predictively competitive algorithms with many human-impact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy. |
| title | Machine Learning Training Optimization using the Barycentric Correction Procedure |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.00542 |