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To fill up these gaps, we propose a Multi-layer Multi-view Stacking Integration (MLMVS) approach to predict default risk in the P2P lending scenario. As the main innovation, our proposal explores multi-view learning and soft probability outputs to produce multi-layer integration based on stacking. An interpretable artificial intelligence tool LIME is embedded for interpreting the prediction results. We perform a comprehensive analysis of MLMVS on the Lending Club dataset and conduct comparative experiments to compare it with a number of well-known individual classifiers and ensemble classification methods, which demonstrate the superiority of MLMVS.<\/jats:p>","DOI":"10.3233\/ida-220403","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T12:35:17Z","timestamp":1691152517000},"page":"1457-1475","source":"Crossref","is-referenced-by-count":8,"title":["A multi-layer multi-view stacking model for credit risk assessment"],"prefix":"10.1177","volume":"27","author":[{"given":"Wenfang","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiao","family":"Gu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ling","family":"Jian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/IDA-220403_ref1","unstructured":"A.S. 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