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Credit Scoring Based on the set-valued Identification Method

Journal of Systems Science & Complexity 2020年第5期33卷 1297-1309页

作者： WANG Ximei HU Min ZHAO Yanlong DJEHICHE Boualem Department of Mathematics KTH Royal Institute of TechnologyStockholm 10044Sweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and SystemsScienceChinese Academy of SciencesBeijing 100190China School of Mathernatical Sciences University ofChinese Academy of SciencesBeijing 100190China Department of Mathematics KTH Rogal Institute of TechnologyStockholm 10044Sweden

Credit scoring is one of the key problems in financial risk *** paper studies the credit scoring problem based on the set-valued identification method,which is used to explain the relation between the individual attribute vectors and classification for the credit worthy and credit worthless *** particular,system parameters are estimated by the set-valued identification algorithm based on a given recognition *** order to illustrate the efficiency of the proposed method,practical experiments are conducted for credit card applicants of Australia and credit card holders from Taiwan,*** empirical results show that the set-valued model has a higher prediction accuracy on both small and large numbers of data set compared with logistic regression ***,parameters estimated by the set-valued identification method are more stable,which provide a meaningful and logical explanation for extracting factors that influence the borrowers’credit scorings.

关键词： Credit scoring logistic regression model prediction accuracy set-valued model

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Lithology Classification Based on set-valued Identification Method

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Journal of Systems Science & Complexity 2022年第5期35卷 1637-1652页

作者： LI Jing WU Lifang LU Wenjun WANG Ting KANG Yu FENG Deyong ZHOU Hansheng Department of Automation University of Science and Technology of ChinaHefei 230027China Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100190China Institute of Artificial Intelligence University of Science and Technology BeijingBeijing 100083China Shengli Geophysical Research Institute SINOPEC GroupDongying 257022China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China

Lithology classification using well logs plays a key role in reservoir *** paper studies the problem of lithology identification based on the set-valued method(SV),which uses the SV model to establish the relation between logging data and lithologic types at a certain depth *** particular,the system model is built on the assumption that the noise between logging data and lithologic types is normally distributed,and then the system parameters are estimated by SV method based on the existing identification *** logging data of Shengli Oilfield in Jiyang Depression are used to verify the effectiveness of SV *** results indicate that the SV model classifies lithology more accurately than the Logistic Regression model(LR)and more stably than uninterpretable models on imbalanced ***,the Macro-F1 of the SV models(i.e.,SV(3),SV(5),and SV(7))are higher than 85%,where the sandstone samples account for only 22%.In addition,the SV(7)lithology identification system achieves the best stability,which is of great practical significance to reservoir exploration.

关键词： DT lithology classification LR RF set-valued model SVM

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