Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916459833196544 |
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| author | Yu, Runlong Ladwig, Robert Xu, Xiang Zhu, Peijun Hanson, Paul C. Xie, Yiqun Jia, Xiaowei |
| author_facet | Yu, Runlong Ladwig, Robert Xu, Xiang Zhu, Peijun Hanson, Paul C. Xie, Yiqun Jia, Xiaowei |
| contents | Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-population Cognitive Evolutionary Search (MCES), a novel evolutionary algorithm for complex feature interaction selection problems. MCES allows models within every population to evolve adaptively, selecting relevant feature interactions for different lake types and tasks. Evaluated on diverse lakes in the Midwestern USA, MCES not only consistently produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lake types, embodying the innovative concept of "AI from nature, for nature". |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_18923 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes Yu, Runlong Ladwig, Robert Xu, Xiang Zhu, Peijun Hanson, Paul C. Xie, Yiqun Jia, Xiaowei Neural and Evolutionary Computing Artificial Intelligence Machine Learning Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-population Cognitive Evolutionary Search (MCES), a novel evolutionary algorithm for complex feature interaction selection problems. MCES allows models within every population to evolve adaptively, selecting relevant feature interactions for different lake types and tasks. Evaluated on diverse lakes in the Midwestern USA, MCES not only consistently produces accurate predictions with few observed labels but also, through gene maps of models, reveals sophisticated phenological patterns of different lake types, embodying the innovative concept of "AI from nature, for nature". |
| title | Evolution-based Feature Selection for Predicting Dissolved Oxygen Concentrations in Lakes |
| topic | Neural and Evolutionary Computing Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2403.18923 |