نمایش مختصر رکورد

dc.contributor.authorKianian, saharen_US
dc.contributor.authorFarzi, saeeden_US
dc.date.accessioned1400-03-20T16:18:05Zfa_IR
dc.date.accessioned2021-06-10T16:18:05Z
dc.date.available1400-03-20T16:18:05Zfa_IR
dc.date.available2021-06-10T16:18:05Z
dc.date.issued2020-08-01en_US
dc.date.issued1399-05-11fa_IR
dc.date.submitted2019-06-27en_US
dc.date.submitted1398-04-06fa_IR
dc.identifier.citationKianian, sahar, Farzi, saeed. (2020). Assessment of Customer Credit Risk using an Adaptive Neuro-Fuzzy System. Computer and Knowledge Engineering, 2(2), 19-28. doi: 10.22067/cke.v2i2.80359en_US
dc.identifier.urihttps://dx.doi.org/10.22067/cke.v2i2.80359
dc.identifier.urihttps://cke.um.ac.ir/article_26988.html
dc.identifier.urihttps://iranjournals.nlai.ir/handle/123456789/830853
dc.description.abstractGiven the financial crises in the world, one of the most important issues of banking industry is the assessment of customers' credit to distinguish bad credit customers from good credit customers. The problem of customer credit risk assessment is a binary classification problem, which suffers from the lack of data and sophisticated features as main challenges. In this paper, an adaptive neuro-fuzzy inference system is exploited to tackle the customer credit risk assessment problem regarding the mentioned challenges. First of all, a SOMTE-based algorithm is introduced to overcome the data imbalancing problem. Then, several efficient features are identified using a MEMETIC meta-heuristic algorithm, and finally an adaptive neuro-fuzzy system is exploited for distinguishing bad credit customers from good ones. To evaluate and compare the performance of the proposed system, the standard German credit data dataset and the well-known classification algorithms are utilized. The results indicate the superiority of the proposed system compared to some well-known algorithms in terms of precision, accuracy, and Type II errors.en_US
dc.format.extent520
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_US
dc.relation.ispartofComputer and Knowledge Engineeringen_US
dc.relation.isversionofhttps://dx.doi.org/10.22067/cke.v2i2.80359
dc.subjectBankingen_US
dc.subjectCustomer credit risken_US
dc.subjectRisk Assessmenten_US
dc.subjectFuzzy systemen_US
dc.titleAssessment of Customer Credit Risk using an Adaptive Neuro-Fuzzy Systemen_US
dc.typeTexten_US
dc.typeMachine learning-Sadoghien_US
dc.contributor.departmentrajaee teacher training universityen_US
dc.contributor.departmentKhajeh Nasir toosi university of technologyen_US
dc.citation.volume2
dc.citation.issue2
dc.citation.spage19
dc.citation.epage28


فایل‌های این مورد

Thumbnail

این مورد در مجموعه‌های زیر وجود دارد:

نمایش مختصر رکورد