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dc.contributor.author송재욱-
dc.date.accessioned2019-08-22T05:54:59Z-
dc.date.available2019-08-22T05:54:59Z-
dc.date.issued2019-01-
dc.identifier.citationIEEE ACCESS , v.7 , Page. 16925-16939en_US
dc.identifier.issn2169-3536-
dc.identifier.urihttps://ieeexplore.ieee.org/document/8629867-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/108941-
dc.description.abstractLoan status prediction is an effective tool for investment decisions in peer-to-peer (P2P) lending market. In P2P lending market, most borrowers fulfill the repayment plan; however, some of them fail to pay back their loans. Therefore, an imbalanced classification method can be utilized to discriminate such default borrowers. In this context, the aim of this paper is to propose an investment decision model in P2P lending market which consists of fully paid loans classified via the instance-based entropy fuzzy support vector machine (IEFSVM). IEFSVM is a modified version of the existing entropy fuzzy support vector machine (EFSVM) in terms of an instance-based scheme. IEFSVM can reflect the pattern of nearest neighbors entropy with respect to the change of its size instead of fixing it in unified neighborhood size. Therefore, IEFSVM allows the class change of nearest neighbors in the determination of fuzzy membership. Applying the model to the lending club dataset, we determine loans that are predicted to be fully paid. Then, we also provide a multiple regression model to generate an investment portfolio based on non-default loans that are predicted to yield high returns. Throughout the experiment, the empirical results reveal that IEFSVM outperforms not only EFSVM but also the six other state-of-the-art classifiers including the cost-sensitive adaptive boosting, cost-sensitive random forest, EasyEnsemble, random undersampling boosting, weighted extreme learning machine, and cost-sensitive extreme gradient boosting in terms of loan status classification. Also, the investment performance of the multiple regression model using IEFSVM is higher and more robust than that of two other benchmarks. In this regard, we conclude that the proposed investment model is a decent and practical approach to support decisions in the P2P lending market.en_US
dc.description.sponsorshipThis work was supported by the National Research Foundation of Korea (NRF) grant through the Ministry of Science and ICT, under Grant 2018R1C1B5043835.en_US
dc.language.isoenen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.subjectEntropyen_US
dc.subjectsupport vector machinesen_US
dc.subjectfinancial managementen_US
dc.subjectdecision support systemsen_US
dc.subjectpeer-to-peer lendingen_US
dc.titleApplication of instance-based entropy fuzzy support vector machine in peer-to-peer lending investment decisionen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/ACCESS.2019.2896474-
dc.relation.journalIEEE ACCESS-
dc.contributor.googleauthorCho, Poongjin-
dc.contributor.googleauthorChang, Woojin-
dc.contributor.googleauthorSong, Jae Wook-
dc.relation.code2019036307-
dc.sector.campusS-
dc.sector.daehakCOLLEGE OF ENGINEERING[S]-
dc.sector.departmentDEPARTMENT OF INDUSTRIAL ENGINEERING-
dc.identifier.pidjwsong-
dc.identifier.researcherIDQ-9826-2019-
dc.identifier.orcidhttp://orcid.org/0000-0001-6455-6524-


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