Generalized Estimating Equations Boosting (GEEB) machine for correlated data
Abstract Rapid development in data science enables machine learning and artificial intelligence to be the most popular research tools across various disciplines. While numerous articles have shown decent predictive ability, little research has examined the impact of complex correlated data. We aim t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2024-01-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40537-023-00875-5 |
| _version_ | 1797276477238018048 |
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| author | Yuan-Wey Wang Hsin-Chou Yang Yi-Hau Chen Chao-Yu Guo |
| author_facet | Yuan-Wey Wang Hsin-Chou Yang Yi-Hau Chen Chao-Yu Guo |
| author_sort | Yuan-Wey Wang |
| collection | DOAJ |
| description | Abstract Rapid development in data science enables machine learning and artificial intelligence to be the most popular research tools across various disciplines. While numerous articles have shown decent predictive ability, little research has examined the impact of complex correlated data. We aim to develop a more accurate model under repeated measures or hierarchical data structures. Therefore, this study proposes a novel algorithm, the Generalized Estimating Equations Boosting (GEEB) machine, to integrate the gradient boosting technique into the benchmark statistical approach that deals with the correlated data, the generalized Estimating Equations (GEE). Unlike the previous gradient boosting utilizing all input features, we randomly select some input features when building the model to reduce predictive errors. The simulation study evaluates the predictive performance of the GEEB, GEE, eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) across several hierarchical structures with different sample sizes. Results suggest that the new strategy GEEB outperforms the GEE and demonstrates superior predictive accuracy than the SVM and XGBoost in most situations. An application to a real-world dataset, the Forest Fire Data, also revealed that the GEEB reduced mean squared errors by 4.5% to 25% compared to GEE, XGBoost, and SVM. This research also provides a freely available R function that could implement the GEEB machine effortlessly for longitudinal or hierarchical data. |
| first_indexed | 2024-03-07T15:28:44Z |
| format | Article |
| id | doaj.art-6f7c6b8fb6e844b0b4e0aa1437e3fdfa |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-03-07T15:28:44Z |
| publishDate | 2024-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-6f7c6b8fb6e844b0b4e0aa1437e3fdfa2024-03-05T16:32:35ZengSpringerOpenJournal of Big Data2196-11152024-01-0111111910.1186/s40537-023-00875-5Generalized Estimating Equations Boosting (GEEB) machine for correlated dataYuan-Wey Wang0Hsin-Chou Yang1Yi-Hau Chen2Chao-Yu Guo3Division of Biostatistics and Data Science, Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung UniversityInstitute of Statistical Science, Academia SinicaInstitute of Statistical Science, Academia SinicaDivision of Biostatistics and Data Science, Institute of Public Health, College of Medicine, National Yang Ming Chiao Tung UniversityAbstract Rapid development in data science enables machine learning and artificial intelligence to be the most popular research tools across various disciplines. While numerous articles have shown decent predictive ability, little research has examined the impact of complex correlated data. We aim to develop a more accurate model under repeated measures or hierarchical data structures. Therefore, this study proposes a novel algorithm, the Generalized Estimating Equations Boosting (GEEB) machine, to integrate the gradient boosting technique into the benchmark statistical approach that deals with the correlated data, the generalized Estimating Equations (GEE). Unlike the previous gradient boosting utilizing all input features, we randomly select some input features when building the model to reduce predictive errors. The simulation study evaluates the predictive performance of the GEEB, GEE, eXtreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM) across several hierarchical structures with different sample sizes. Results suggest that the new strategy GEEB outperforms the GEE and demonstrates superior predictive accuracy than the SVM and XGBoost in most situations. An application to a real-world dataset, the Forest Fire Data, also revealed that the GEEB reduced mean squared errors by 4.5% to 25% compared to GEE, XGBoost, and SVM. This research also provides a freely available R function that could implement the GEEB machine effortlessly for longitudinal or hierarchical data.https://doi.org/10.1186/s40537-023-00875-5Correlated dataHierarchical dataGeneralized Estimating EquationsMachine learningGradient boosting |
| spellingShingle | Yuan-Wey Wang Hsin-Chou Yang Yi-Hau Chen Chao-Yu Guo Generalized Estimating Equations Boosting (GEEB) machine for correlated data Journal of Big Data Correlated data Hierarchical data Generalized Estimating Equations Machine learning Gradient boosting |
| title | Generalized Estimating Equations Boosting (GEEB) machine for correlated data |
| title_full | Generalized Estimating Equations Boosting (GEEB) machine for correlated data |
| title_fullStr | Generalized Estimating Equations Boosting (GEEB) machine for correlated data |
| title_full_unstemmed | Generalized Estimating Equations Boosting (GEEB) machine for correlated data |
| title_short | Generalized Estimating Equations Boosting (GEEB) machine for correlated data |
| title_sort | generalized estimating equations boosting geeb machine for correlated data |
| topic | Correlated data Hierarchical data Generalized Estimating Equations Machine learning Gradient boosting |
| url | https://doi.org/10.1186/s40537-023-00875-5 |
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