Designing a hybrid intelligent mining system for credit risk evaluation
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Publication:732812
DOI10.1007/s11424-008-9133-7zbMath1177.91139OpenAlexW2045657836MaRDI QIDQ732812
Kin Keung Lai, Lean Yu, Shaoyi He, Shou-Yang Wang, Feng-Hua Wen
Publication date: 15 October 2009
Published in: Journal of Systems Science and Complexity (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11424-008-9133-7
Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37) Credit risk (91G40)
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Cites Work
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- Prediction of company acquisition in Greece by means of the rough set approach
- Business failure prediction using rough sets
- Mining classification rules using rough sets and neural networks
- Rough classification
- Rule Extraction from Support Vector Machines
- Rough sets
- Rough set methods in feature selection and recognition
- Information-theoretic algorithm for feature selection
- Linear and Nonlinear Separation of Patterns by Linear Programming
- A survey of the issues in consumer credit modelling research
- Credit Risk Evaluation with Least Square Support Vector Machine
- Reducts within the variable precision rough sets model: A further investigation
- Differentiating between good credits and bad credits using neuro-fuzzy systems
- Using rough sets with heuristics for feature selection
- Gene selection for cancer classification using support vector machines