Model Parameter-Based Transfer Learning for ESG Score Prediction in Developing Markets.
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| Title: | Model Parameter-Based Transfer Learning for ESG Score Prediction in Developing Markets. |
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| Authors: | Marković, Ivana1 ivana.markovic@eknfak.ni.ac.rs, Ljajić, Adela2 adela.ljajic@ivi.ac.rs, Stanković, Jelena Z.1 jelenas@eknfak.ni.ac.rs.jovica, Košprdić, Miloš2 milos.kosprdic@ivi.ac.rs, Stanković, Jovica1 stankovic@eknfak.ni.ac.rs |
| Source: | Computer Science & Information Systems. Jun2026, Vol. 23 Issue 3, p969-1000. 32p. |
| Subjects: | Environmental, social, & governance factors, Random forest algorithms, Machine learning, Knowledge transfer, Emerging markets, Sustainable investing |
| Abstract: | While ESG (Environmental, Social, and Governance) assessment plays a key role in sustainable finance, data scarcity and noise in emerging economies hinder robust model development. To address this, we propose a model para meter based transfer learning with random forest (MPBTL-RF) approach for domain adaptation situations where source data are not available. The proposed model is evaluated using three traditional learning approaches: Random Forest (RF), eXtreme Gradient Boosting (XGB), and Feedforward Neural Networks (FNN). Cross-validation is used to assess model generalizability, and domain adaptation is tested through indomain and out-of-domain settings. The proposed MPBTL-RF approach achieves competitive performance compared to traditional baselines in scenarios with limited training data, offering time advantages with predictive efficiency and stability. This work demonstrates how machine learning pipelines can adapt to data-constrained, real-world domains, fostering the synergy between AI (Artificial Intelligence) and business. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195162951 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Model Parameter-Based Transfer Learning for ESG Score Prediction in Developing Markets. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Marković%2C+Ivana%22">Marković, Ivana</searchLink><relatesTo>1</relatesTo><i> ivana.markovic@eknfak.ni.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Ljajić%2C+Adela%22">Ljajić, Adela</searchLink><relatesTo>2</relatesTo><i> adela.ljajic@ivi.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Stanković%2C+Jelena+Z%2E%22">Stanković, Jelena Z.</searchLink><relatesTo>1</relatesTo><i> jelenas@eknfak.ni.ac.rs.jovica</i><br /><searchLink fieldCode="AR" term="%22Košprdić%2C+Miloš%22">Košprdić, Miloš</searchLink><relatesTo>2</relatesTo><i> milos.kosprdic@ivi.ac.rs</i><br /><searchLink fieldCode="AR" term="%22Stanković%2C+Jovica%22">Stanković, Jovica</searchLink><relatesTo>1</relatesTo><i> stankovic@eknfak.ni.ac.rs</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Science+%26+Information+Systems%22">Computer Science & Information Systems</searchLink>. Jun2026, Vol. 23 Issue 3, p969-1000. 32p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Environmental%2C+social%2C+%26+governance+factors%22">Environmental, social, & governance factors</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+transfer%22">Knowledge transfer</searchLink><br /><searchLink fieldCode="DE" term="%22Emerging+markets%22">Emerging markets</searchLink><br /><searchLink fieldCode="DE" term="%22Sustainable+investing%22">Sustainable investing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: While ESG (Environmental, Social, and Governance) assessment plays a key role in sustainable finance, data scarcity and noise in emerging economies hinder robust model development. To address this, we propose a model para meter based transfer learning with random forest (MPBTL-RF) approach for domain adaptation situations where source data are not available. The proposed model is evaluated using three traditional learning approaches: Random Forest (RF), eXtreme Gradient Boosting (XGB), and Feedforward Neural Networks (FNN). Cross-validation is used to assess model generalizability, and domain adaptation is tested through indomain and out-of-domain settings. The proposed MPBTL-RF approach achieves competitive performance compared to traditional baselines in scenarios with limited training data, offering time advantages with predictive efficiency and stability. This work demonstrates how machine learning pipelines can adapt to data-constrained, real-world domains, fostering the synergy between AI (Artificial Intelligence) and business. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Science & Information Systems is the property of ComSIS Consortium and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.2298/CSIS250616028M Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 969 Subjects: – SubjectFull: Environmental, social, & governance factors Type: general – SubjectFull: Random forest algorithms Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Knowledge transfer Type: general – SubjectFull: Emerging markets Type: general – SubjectFull: Sustainable investing Type: general Titles: – TitleFull: Model Parameter-Based Transfer Learning for ESG Score Prediction in Developing Markets. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Marković, Ivana – PersonEntity: Name: NameFull: Ljajić, Adela – PersonEntity: Name: NameFull: Stanković, Jelena Z. – PersonEntity: Name: NameFull: Košprdić, Miloš – PersonEntity: Name: NameFull: Stanković, Jovica IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18200214 Numbering: – Type: volume Value: 23 – Type: issue Value: 3 Titles: – TitleFull: Computer Science & Information Systems Type: main |
| ResultId | 1 |
