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To avoid the curse of dimensionality caused by too many features and identify the key factors in credit risk, the authors remove the irrelevant and redundant features by feature selection. Using the data provided by Prosper.com, one of the biggest P2P lending platforms in the world, they show that: (1) it can achieve better performance, measured by both AUC (area under the receiver operating characteristic curve) and classification accuracy, by fusion of information from different data sources; (2) it requires only ten features from different data sources to get better performance.<\/jats:p>","DOI":"10.4018\/ijssci.2017040101","type":"journal-article","created":{"date-parts":[[2017,4,7]],"date-time":"2017-04-07T15:30:00Z","timestamp":1491579000000},"page":"1-13","source":"Crossref","is-referenced-by-count":9,"title":["Feature Engineering for Credit Risk Evaluation in Online P2P Lending"],"prefix":"10.4018","volume":"9","author":[{"given":"Shuxia","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing Institute of Petrochemical Technology, Beijing, China"}]},{"given":"Bin","family":"Fu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Hongzhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Zhengshen","family":"Jiang","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"Zhonghai","family":"Wu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"given":"D. 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