A Survey of Applying Machine Learning Techniques for Credit Rating: Existing Models and Open Issues
| dc.contributor.author | Xu, Min | |
| dc.contributor.author | Pusatli, Ozgur Tolga | |
| dc.contributor.author | Wang, Xiang | |
| dc.date.accessioned | 2020-05-06T19:31:17Z | |
| dc.date.accessioned | 2025-09-18T12:04:59Z | |
| dc.date.available | 2020-05-06T19:31:17Z | |
| dc.date.available | 2025-09-18T12:04:59Z | |
| dc.date.issued | 2015 | |
| dc.description | Pusatli, Tolga/0000-0002-2303-8023; Xu, Min/0000-0001-9581-8849 | en_US |
| dc.description.abstract | In recent years, machine learning techniques have been widely applied for credit rating. To make a rational comparison of performance of different learning-based credit rating models, we focused on those models that are constructed and validated on the two mostly used Australian and German credit approval data sets. Based on a systematic review of literatures, we further compare and discuss about the performance of existing models. In addition, we identified and illustrated the limitations of existing works and discuss about some open issues that could benefit future research in this area. | en_US |
| dc.identifier.citation | Wang, X.; Xu, M.; Pusatli, Ö.T., "A Survey of Applying Machine Learning Techniques for Credit Rating: Existing Models and Open Issues", Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 9490, (2015). | en_US |
| dc.identifier.doi | 10.1007/978-3-319-26535-3_15 | |
| dc.identifier.isbn | 9783319265353 | |
| dc.identifier.isbn | 9783319265346 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.issn | 1611-3349 | |
| dc.identifier.scopus | 2-s2.0-84951731069 | |
| dc.identifier.uri | https://doi.org/10.1007/978-3-319-26535-3_15 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12416/10476 | |
| dc.language.iso | en | en_US |
| dc.publisher | Springer international Publishing Ag | en_US |
| dc.relation.ispartof | 22nd International Conference on Neural Information Processing (ICONIP) -- NOV 09-12, 2015 -- Istanbul, TURKEY | en_US |
| dc.relation.ispartofseries | Lecture Notes in Computer Science | |
| dc.rights | info:eu-repo/semantics/closedAccess | en_US |
| dc.subject | Credit Rating | en_US |
| dc.subject | Single Classifier Models | en_US |
| dc.subject | Hybrid Learning Models | en_US |
| dc.subject | Literature Survey | en_US |
| dc.title | A Survey of Applying Machine Learning Techniques for Credit Rating: Existing Models and Open Issues | en_US |
| dc.title | A Survey of Applying Machine Learning Techniques for Credit Rating: Existing Models and Open Issues | tr_TR |
| dc.type | Conference Object | en_US |
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| gdc.author.id | Pusatli, Tolga/0000-0002-2303-8023 | |
| gdc.author.id | Xu, Min/0000-0001-9581-8849 | |
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| gdc.description.department | Çankaya University | en_US |
| gdc.description.departmenttemp | [Wang, Xiang; Xu, Min] Univ Technol Sydney, Global Big Data Technol Ctr, Sydney, NSW 2007, Australia; [Pusatli, Ozgur Tolga] Cankaya Univ, Dept Math & Comp Sci, Ankara, Turkey | en_US |
| gdc.description.endpage | 132 | en_US |
| gdc.description.publicationcategory | Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı | en_US |
| gdc.description.scopusquality | Q3 | |
| gdc.description.startpage | 122 | en_US |
| gdc.description.volume | 9490 | en_US |
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| gdc.virtual.author | Pusatlı, Özgür Tolga | |
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