Faculty of Business Administration

AIM Seminar

Data-Trait-Driven Big Data Credit Classification for Credit Services


Prof. Lean Yu


School of Economics and Management

Beijing University of Chemical Technology



In this talk, a general framework of data-trait-driven big data credit risk classification is proposed. In the proposed framework, an integrated data trait identification scheme are first presented for multivariate credit data classification so as to identify corresponding traits existed in datasets. Then, based on the nature of these traits, corresponding identification and solution methods are proposed in detail. Finally, some most appropriate classification methods are selected to obtain perfect credit classification results in terms of the data traits hidden in the datasets. For further illustration, four publicly used credit datasets are selected as sample data to test the applicability of data traits identification scheme proposed in this paper. At the same time, two classification experiments based on different data traits are conducted to verify the effectiveness of the proposed data-trait-driven credit classification methodology. Empirical results reveal that data traits existed in all datasets can be identified clearly and accordingly suitable methods in terms of the data traits can be selected to handle them in the framework of the proposed solutions, indicating that the proposed framework can be used as an effective tool for multivariate credit data classification.


Date:          10 Dec 2019 (Tue)

Time:          14:00-15:00

Venue:       N1-1004


A Short Biography

Prof. Lean Yu received his Ph.D. degree in Management Sciences and Engineering from Academy of Mathematics and Systems Science, Chinese Academy of Sciences (CAS) in 2005. He is currently a professor and PhD supervisor of School of Economics and Management, Beijing University of Chemical Technology. He is a winner of National Science Fund for Distinguished Young Scholars, National Program for Support of Top-Notch Young Professionals and “Hundred Talents Program” of Chinese Academy of Sciences. He is acted as a guest editor, managing editor, associate editor and editorial members of many international journals including Computers & Operations Research and Journal of Computer Science. So far, he has published five monographs (two monographs have been published by Springer-Verlag) and about 100 SCI/SSCI articles in some top journals including IEEE Transactions on Evolutionary Computation and IEEE Transactions on Knowledge and Data Engineering. At the same time, he received many awards and honors, such as “Elsevier Most Cited Chinese Researchers” from Elsevier, “IAOI Outstanding Professor” from International Association of Organization Innovation (IAOI), “China Youth Science and Technology Award” from the Organization Department of the Central Committee of the CPC, “The 100 National Best PhD Theses Award” from Academic Degrees Committee of State Council and Ministry of Education of China, “First Class Prize for Beijing Science and Technology Award” from the Beijing Municipal Government, First Class Prize for Natural Science Award of Ministry of Education (MOE), Beijing Mao Yisheng Youth Science and Technology Award, and “Lu Jia-xi Young Talent Award” of Chinese Academy of Sciences. His research interests include business intelligence, big data mining, economic forecasting and intelligent financial management.