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DC Field | Value | Language |
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dc.contributor.author | Zhu, Y | - |
dc.contributor.author | Zhou, L | - |
dc.contributor.author | Xie, C | - |
dc.contributor.author | Wang, G-J | - |
dc.contributor.author | Nguyen, TV | - |
dc.date.accessioned | 2019-03-28T11:54:22Z | - |
dc.date.available | 2019-01-28 | - |
dc.date.available | 2019-03-28T11:54:22Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Zhu, Y., Zhou, L., Xie, C., Wang, G.-J. and Nguyen, T.V. (2019). Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211, pp.22–33. doi: 10.1016/j.ijpe.2019.01.032 | en_US |
dc.identifier.issn | 0925-5273 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/17809 | - |
dc.description.sponsorship | National Natural Science Foundation of China | en_US |
dc.format.extent | 22 - 33 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Supply chain finance | en_US |
dc.subject | Small and medium-sized enterprises | en_US |
dc.subject | Credit risk forecasting | en_US |
dc.subject | Machine learning | en_US |
dc.subject | RS-MultiBoosting | en_US |
dc.subject | Partial dependency plot | en_US |
dc.title | Forecasting SMEs’ credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1016/j.ijpe.2019.01.032 | - |
dc.relation.isPartOf | International Journal of Production Economics | - |
pubs.notes | keywords: Supply chain finance, Small and medium-sized enterprises, Credit risk forecasting, Machine learning, RS-MultiBoosting, Partial dependency plot | - |
pubs.publication-status | Published | - |
pubs.volume | 211 | - |
Appears in Collections: | Brunel Business School Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Embargoed until 28 Jan 2020 | 1.49 MB | Adobe PDF | View/Open |
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