首页|New Machine Learning Findings from University of York Described (Forecasting Smes’ Credit Risk In Supply Chain Finance With a Sampling Strategy Based On Machine Learning Techniques)
New Machine Learning Findings from University of York Described (Forecasting Smes’ Credit Risk In Supply Chain Finance With a Sampling Strategy Based On Machine Learning Techniques)
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Springer Nature
Research findings on Machine Learning are discussed in a new report. According to news reporting originating in York, United Kingdom, by NewsRx journalists, research stated, “Exploring the value of multi-source information fusion to predict small and medium-sized enterprises’ (SMEs) credit risk in supply chain finance (SCF) is a popular yet challenging task, as two issues of key variable selection and imbalanced class must be addressed simultaneously. To this end, we develop new forecast models adopting an imbalance sampling strategy based on machine learning techniques and apply these new models to predict credit risk of SMEs in China, using financial information, operation information, innovation information, and negative events as predictors.” Funders for this research include National Natural Science Foundation of China (NSFC), Humanity and Social Science Foundation of Ministry of Education of China, Fundamental Research Funds for the Central Universities, China Postdoctoral Science Foundation.
YorkUnited KingdomEuropeCyborgsEmerging TechnologiesMachine LearningUniversity of York
NETL
NSTL
Springer Nature
