An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection

Wu，Yiqiong1; Huang，Wei1,2; Tian，Yingjie3; Zhu，Qing4; Yu，Lean5,6

2022-05-01

10.1016/j.elerap.2022.101155

Electronic Commerce Research and Applications   影响因子和分区

1567-4223

1873-7846

In order to solve the problem of uncertain misclassification costs and class distributions in credit scoring tasks, an uncertainty-oriented credit scoring framework based on a multi-objective feature selection strategy is proposed in this study. This proposed framework searches for a pool of Pareto-optimal credit scoring models with different feature subsets without the assumption of the operating condition (misclassification costs and class distributions) information. Specifically, the searching process concerns the trade-off of the False Positive Rate and the False Negative Rate using a binary multi-objective particle swarm optimization (BMOPSO) algorithm. By visualizing the Pareto-optimal solutions in cost space, credit decision-makers can select an optimal compromise model based on their decision-making contexts. The proposed framework is compared with baseline models on three retail credit scoring datasets. The experimental results show that the proposed framework could find out the optimal credit model with minimal misclassification cost for almost all possible operating conditions.

Credit scoring Feature selection Multi-objective optimization Uncertain decision making

SCI ; EI ; SSCI

英语

通讯

NSFC[72061127002,"2018WZDXM020"] ; NSFC[72061127002,"2018WZDXM020",71731009] ; Major Program of the National Social Science Foundation of China[72061127002] ; Key Program of Research Center of Scientific Finance and Entrepreneurial Finance of Ministry of Education of Sichuan Province[2018WZDXM020] ; [19ZDA103] ; [KJJR2021-003]

Business & Economics ; Computer Science

Business ; Computer Science, Information Systems ; Computer Science, Interdisciplinary Applications

WOS:000808336200002

ELSEVIER

20222212185644

Economic and social effects ; Feature extraction ; Multiobjective optimization ; Pareto principle ; Particle swarm optimization (PSO)

Computer Software, Data Handling and Applications:723 ; Management:912.2 ; Optimization Techniques:921.5 ; Social Sciences:971

2-s2.0-85131092231

Scopus

1.School of Management,Xi'an Jiaotong University,Xi'an,710049,China
2.College of Business,Southern University of Science and Technology,Shenzhen,518055,China
3.School of Economics and Management,University of Chinese Academy of Sciences,Beijing,100049,China
4.International Business School,Shaanxi Normal University,Xi'an,710119,China
5.Weiqiao-UCAS Joint Lab,University of Chinese Academy of Sciences,Beijing,100190,China
6.School of Business,Binzhou Institute of Technology,Binzhou,256600,China

Wu，Yiqiong,Huang，Wei,Tian，Yingjie,et al. An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection[J]. Electronic Commerce Research and Applications,2022,53.

Wu，Yiqiong,Huang，Wei,Tian，Yingjie,Zhu，Qing,&Yu，Lean.(2022).An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection.Electronic Commerce Research and Applications,53.

Wu，Yiqiong,et al."An uncertainty-oriented cost-sensitive credit scoring framework with multi-objective feature selection".Electronic Commerce Research and Applications 53(2022).