Ying Zhou is an Associate Professor, a PhD Supervisor and a doctor of management science and engineering, and she works in the School of Economics and Management of Dalian University of Technology, Dalian 116024, China. Her current research interest is the theory and method of credit risk assessment based on big data. She has hosted one general project of the National Natural Science Foundation of China, one general project of the National Social Science Foundation, and some other nation-level and province-level projects. She has published and accepted several papers in the SSCI index journals, and the "Journal of Management Science", "Journal of System Management" and other Rank-A journals recognized by the Ministry of Management Science of the National Natural Science Foundation of China.
Research on listed companies’ credit ratings, considering classification performance and interpretability
This study uses the correlation coefficient and F-test to select the initial features of a credit evaluation system, and then a validity index for a second selection to ensure that the feature system has the optimum ability to discriminate in determining…
This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world.
Determination of weights for an optimal credit rating model based on default and nondefault distance maximization
This study proposes a credit rating model that accurately identifies default and nondefault companies by maximizing intergroup credit score deviations and minimizing intragroup deviations.
This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification and regression tree (CART), logistic regression (LR), multivariate adaptive regression splines (MARS) and a random forest (RF).