Support vector machines for default prediction of SMEs based on technology credit

DOI10.1016/j.ejor.2009.03.036zbMath1173.90458OpenAlexW2079492342MaRDI QIDQ1038344

So Young Sohn, Hong Sik Kim

Publication date: 17 November 2009

Published in: European Journal of Operational Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ejor.2009.03.036

zbMATH Keywords

support vector machinessmall and medium enterprisesdefault prediction model

Mathematics Subject Classification ID

Management decision making, including multiple objectives (90B50) Case-oriented studies in operations research (90B90) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (12)

Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms -- support vector regression forecast combinations ⋮ Instance-based credit risk assessment for investment decisions in P2P lending ⋮ Variable selection in classification model via quadratic programming ⋮ Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis ⋮ Robust cost-sensitive kernel method with Blinex loss and its applications in credit risk evaluation ⋮ A transformer-based model for default prediction in mid-cap corporate markets ⋮ European exchange trading funds trading with locally weighted support vector regression ⋮ A corporate credit rating model using support vector domain combined with fuzzy clustering algorithm ⋮ Behaviour-based short-term invoice probability of default evaluation ⋮ Updating a credit-scoring model based on new attributes without realization of actual data ⋮ A corporate credit rating model using multi-class support vector machines with an ordinal pairwise partitioning approach ⋮ Social collateral, soft information and online peer-to-peer lending: a theoretical model

Uses Software

• LIBSVM

Cites Work

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• Learning distributions by their density levels: A paradigm for learning without a teacher
• Neural network credit scoring models
• Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis
• Support-vector networks
• Random effects logistic regression model for default prediction of technology credit guarantee fund
• Technology scoring model for reflecting evaluator's perception within confidence limits
• Technology scoring model considering rejected applicants and effect of reject inference
• The risk management for technology credit guarantee fund
• Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
• Differentiating between good credits and bad credits using neuro-fuzzy systems

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