乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T09:15:55Z","timestamp":1688030155281},"reference-count":28,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2021,1,11]],"date-time":"2021-01-11T00:00:00Z","timestamp":1610323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JSIT"],"published-print":{"date-parts":[[2022,4,11]]},"abstract":"\nPurpose<\/jats:title>\nThis study aims to assess the default risk of borrowers in peer-to-peer (P2P) online lending platforms. The authors propose a novel default risk classification model based on data cleaning and feature extraction, which increases risk assessment accuracy.<\/jats:p>\n<\/jats:sec>\n\nDesign\/methodology\/approach<\/jats:title>\nThe authors use borrower data from the Lending Club and propose the risk assessment model based on low-rank representation (LRR) and discriminant analysis. Firstly, the authors use three LRR models to clean the high-dimensional borrower data by removing outliers and noise, and then the authors adopt a discriminant analysis algorithm to reduce the dimension of the cleaned data. In the dimension-reduced feature space, machine learning classifiers including the k<\/jats:italic>-nearest neighbour, support vector machine and artificial neural network are used to assess and classify default risks.<\/jats:p>\n<\/jats:sec>\n\nFindings<\/jats:title>\nThe results reveal significant noise and redundancy in the borrower data. LRR models can effectively clean such data, particularly the two LRR models with local manifold regularisation. In addition, the supervised discriminant analysis model, termed the local Fisher discriminant analysis model, can extract low-dimensional and discriminative features, which further increases the accuracy of the final risk assessment models.<\/jats:p>\n<\/jats:sec>\n\nOriginality\/value<\/jats:title>\nThe originality of this study is that it proposes a novel default risk assessment model, based on data cleaning and feature extraction, for P2P online lending platforms. The proposed approach is innovative and efficient in the P2P online lending field.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/jsit-03-2020-0040","type":"journal-article","created":{"date-parts":[[2021,1,13]],"date-time":"2021-01-13T09:04:20Z","timestamp":1610528660000},"page":"96-111","source":"Crossref","is-referenced-by-count":1,"title":["Low rank representation and discriminant analysis-based models for peer-to-peer default risk assessment"],"prefix":"10.1108","volume":"24","author":[{"given":"Gui","family":"Yuan","sequence":"first","affiliation":[]},{"given":"Shali","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Fu","sequence":"additional","affiliation":[]},{"given":"Xinwei","family":"Jiang","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,1,11]]},"reference":[{"issue":"8","key":"key2022040810120390700_ref001","doi-asserted-by":"publisher","first-page":"3825","DOI":"10.1016\/j.eswa.2013.12.003","article-title":"Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring","volume":"41","year":"2014","journal-title":"Expert Systems with Applications"},{"issue":"10","key":"key2022040810120390700_ref002","doi-asserted-by":"publisher","first-page":"e0139427","DOI":"10.1371\/journal.pone.0139427","article-title":"Determinants of default in P2P lending","volume":"10","year":"2015","journal-title":"PLoS One"},{"key":"key2022040810120390700_ref003","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1016\/j.najef.2019.101013","article-title":"Inferences of default risk and borrower characteristics on P2P lending","volume":"50","year":"2019","journal-title":"The North American Journal of Economics and Finance"},{"key":"key2022040810120390700_ref004","first-page":"2859","article-title":"Linear dimensionality reduction: survey, insights, and generalizations","volume":"16","year":"2015","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"key2022040810120390700_ref005","first-page":"37","article-title":"Peer to peer lending: structures, risks and regulation","year":"2016","journal-title":"JASSA: The Finsia Journal of Applied Finance"},{"key":"key2022040810120390700_ref006","doi-asserted-by":"publisher","first-page":"5562","DOI":"10.24251\/HICSS.2017.671","article-title":"Borrower\u2019s self-disclosure of social media information in P2P lending","volume-title":"Proceedings of the 50th HI International Conference on System Sciences","year":"2017"},{"issue":"2","key":"key2022040810120390700_ref007","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1016\/j.ejor.2015.05.050","article-title":"Instance-based credit risk assessment for investment decisions in P2P lending","volume":"249","year":"2016","journal-title":"European Journal of Operational Research"},{"key":"key2022040810120390700_ref008","doi-asserted-by":"publisher","first-page":"511","DOI":"10.23919\/ICACT.2019.8701943","article-title":"Improving credit risk prediction in online peer-to-peer (P2P) lending using feature selection with deep learning","volume-title":"Proceedings of the 21st International Conference on Advanced Communication Technology (ICACT)","year":"2019"},{"issue":"8","key":"key2022040810120390700_ref009","doi-asserted-by":"publisher","first-page":"2455","DOI":"10.1093\/rfs\/hhs071","article-title":"Trust and credit: the role of appearance in peer-to-peer lending","volume":"25","year":"2012","journal-title":"Review of Financial Studies"},{"key":"key2022040810120390700_ref010","first-page":"1783","article-title":"Probabilistic non-linear principal component analysis with Gaussian process latent variable models","volume":"8","year":"2005","journal-title":"Journal of Machine Learning Research"},{"issue":"3","key":"key2022040810120390700_ref011","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1108\/CFRI-06-2017-0156","article-title":"The mechanism and effectiveness of credit scoring of P2P lending platform: evidence from Renrendai","volume":"8","year":"2018","journal-title":"China Finance Review International"},{"key":"key2022040810120390700_ref012","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.dss.2018.05.001","article-title":"A new aspect on P2P online lending default prediction using Meta-level phone usage data in China","volume":"111","year":"2018","journal-title":"Decision Support Systems"},{"key":"key2022040810120390700_ref013","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.elerap.2018.08.002","article-title":"Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGBoost algorithms according to different high dimensional data cleaning","volume":"31","year":"2018","journal-title":"Electronic Commerce Research and Applications"},{"key":"key2022040810120390700_ref014","doi-asserted-by":"crossref","unstructured":"Maaten, V.D., Postma, E.O. and Herik, V.D. 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