Academic Journal
An Interpretable Semi-Parametric Framework for CMM Export Prediction via Symbolic Regression and Multi-Objective Optimization.
| Τίτλος: | An Interpretable Semi-Parametric Framework for CMM Export Prediction via Symbolic Regression and Multi-Objective Optimization. |
|---|---|
| Συγγραφείς: | Liu, Jinqiu1 (AUTHOR), Xia, Yujie1 (AUTHOR), Zhao, Weixing1 (AUTHOR), Zhang, Dongqing1 (AUTHOR) zhangdq@njau.edu.cn |
| Πηγή: | Mathematics (2227-7390). Jul2026, Vol. 14 Issue 13, p2326. 28p. |
| Θεματικοί όροι: | *Multi-objective optimization, *Bees algorithm, *Regional economics, *Chinese medicine, *Foreign trade promotion, *Regression analysis, *Statistical models |
| Γεωγραφικοί όροι: | China |
| Περίληψη: | Amid the accelerating globalization of Traditional Chinese Medicine (TCM), the export performance of Chinese Medicinal Materials (CMMs) has emerged as a critical indicator of its international competitiveness and systemic resilience. However, existing studies predominantly rely on aggregated national statistics, thereby obscuring substantial inter-provincial heterogeneity and limiting the capacity to capture underlying structural dynamics. To address this challenge, this study conceptualizes CMM export as a complex regional economic system and constructs a panel dataset encompassing 31 provinces in China. We propose a structurally interpretable semi-parametric framework, termed the SR-MOABC-FE model, which integrates symbolic regression (SR) with a fixed effects (FE) specification. Within this framework, SR is employed to flexibly characterize nonlinear relationships, while the FE component accounts for province-specific heterogeneity. To further enhance model performance, a multi-objective artificial bee colony (MOABC) algorithm is developed to simultaneously optimize goodness-of-fit, predictive accuracy, and model parsimony. Empirical results demonstrate that the proposed model achieves a test-set mean absolute percentage error (MAPE) of 9.15%, significantly outperforming both conventional econometric models and mainstream machine learning approaches. Beyond predictive gains, the model retains strong structural interpretability, enabling the identification of key driving factors, including policy support, rural residents' per capita disposable income, and natural resource endowment. Overall, this study advances a hybrid system modeling paradigm that bridges interpretable econometric structures and data-driven symbolic learning, offering both methodological innovation and actionable insights for the analysis and optimization of complex trade systems. [ABSTRACT FROM AUTHOR] |
| Βάση Δεδομένων: | Academic Search Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:asx&genre=article&issn=22277390&ISBN=&volume=14&issue=13&date=20260701&spage=2326&pages=2326-2353&title=Mathematics (2227-7390)&atitle=An%20Interpretable%20Semi-Parametric%20Framework%20for%20CMM%20Export%20Prediction%20via%20Symbolic%20Regression%20and%20Multi-Objective%20Optimization.&aulast=Liu%2C%20Jinqiu&id=DOI:10.3390/math14132326 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Header | DbId: asx DbLabel: Academic Search Index An: 195443900 RelevancyScore: 1431 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1430.76818847656 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Interpretable Semi-Parametric Framework for CMM Export Prediction via Symbolic Regression and Multi-Objective Optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Jinqiu%22">Liu, Jinqiu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xia%2C+Yujie%22">Xia, Yujie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Weixing%22">Zhao, Weixing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Dongqing%22">Zhang, Dongqing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhangdq@njau.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Mathematics+%282227-7390%29%22">Mathematics (2227-7390)</searchLink>. Jul2026, Vol. 14 Issue 13, p2326. 28p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Bees+algorithm%22">Bees algorithm</searchLink><br />*<searchLink fieldCode="DE" term="%22Regional+economics%22">Regional economics</searchLink><br />*<searchLink fieldCode="DE" term="%22Chinese+medicine%22">Chinese medicine</searchLink><br />*<searchLink fieldCode="DE" term="%22Foreign+trade+promotion%22">Foreign trade promotion</searchLink><br />*<searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Statistical+models%22">Statistical models</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Amid the accelerating globalization of Traditional Chinese Medicine (TCM), the export performance of Chinese Medicinal Materials (CMMs) has emerged as a critical indicator of its international competitiveness and systemic resilience. However, existing studies predominantly rely on aggregated national statistics, thereby obscuring substantial inter-provincial heterogeneity and limiting the capacity to capture underlying structural dynamics. To address this challenge, this study conceptualizes CMM export as a complex regional economic system and constructs a panel dataset encompassing 31 provinces in China. We propose a structurally interpretable semi-parametric framework, termed the SR-MOABC-FE model, which integrates symbolic regression (SR) with a fixed effects (FE) specification. Within this framework, SR is employed to flexibly characterize nonlinear relationships, while the FE component accounts for province-specific heterogeneity. To further enhance model performance, a multi-objective artificial bee colony (MOABC) algorithm is developed to simultaneously optimize goodness-of-fit, predictive accuracy, and model parsimony. Empirical results demonstrate that the proposed model achieves a test-set mean absolute percentage error (MAPE) of 9.15%, significantly outperforming both conventional econometric models and mainstream machine learning approaches. Beyond predictive gains, the model retains strong structural interpretability, enabling the identification of key driving factors, including policy support, rural residents' per capita disposable income, and natural resource endowment. Overall, this study advances a hybrid system modeling paradigm that bridges interpretable econometric structures and data-driven symbolic learning, offering both methodological innovation and actionable insights for the analysis and optimization of complex trade systems. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=asx&AN=195443900 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/math14132326 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 2326 Subjects: – SubjectFull: China Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Bees algorithm Type: general – SubjectFull: Regional economics Type: general – SubjectFull: Chinese medicine Type: general – SubjectFull: Foreign trade promotion Type: general – SubjectFull: Regression analysis Type: general – SubjectFull: Statistical models Type: general Titles: – TitleFull: An Interpretable Semi-Parametric Framework for CMM Export Prediction via Symbolic Regression and Multi-Objective Optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Jinqiu – PersonEntity: Name: NameFull: Xia, Yujie – PersonEntity: Name: NameFull: Zhao, Weixing – PersonEntity: Name: NameFull: Zhang, Dongqing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 22277390 Numbering: – Type: volume Value: 14 – Type: issue Value: 13 Titles: – TitleFull: Mathematics (2227-7390) Type: main |
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