Computer Science ›› 2021, Vol. 48 ›› Issue (11A): 71-76.doi: 10.11896/jsjkx.210200110

• Intelligent Computing • Previous Articles     Next Articles

Risk Control Model and Algorithm Based on AP-Entropy Selection Ensemble

WANG Mao-guang, YANG Hang   

  1. School of Information,Central University of Finance and Economics,Beijing 100081,China
  • Online:2021-11-10 Published:2021-11-12
  • About author:WANG Mao-guang,born in 1974,Ph.D,professor.His main research interests include intelligent risk control models and algorithms,big data and intelligent software engineering etc.
    YANG Hang,born in 1997,postgraduate.Her main research interests include intelligent risk control models and algorithms.

Abstract: In recent years,many risk control problems have emerged in the field of Internet finance.For this,we adopt a variety of feature selection methods to preprocess data indicators in the field of risk control,and construct a comprehensive risk control indicator system for corporate credit.And we use stacking ensemble strategy to study credit risk model based on AP-entropy.There are two layers of learners in credit risk model.The idea of selection ensemble is introduced to select the base learners from the category and quantity.First,in machine learning algorithms such as Logistic regression,back propagation neural network,AdaBoost,AP clustering algorithm is used to select a heterogeneous learner suitable for corporate credit risk as the base learner.Se-condly,in each iteration of the learner,entropy is used to select the best learner,and the base learner with the highest F1 value is automatically selected.Among them,the improved algorithm based on entropy improves the efficiency of base learner selection process and reduces the computational cost of the model.Xgboost is selected as the secondary base learner.The empirical results show that the proposed model has good performance and generalization ability.

Key words: Affinity propagation clustering algorithm, AP-Entropy credit risk model, Learner selection algorithm based on Entropy, Risk control feature system, Selective ensemble, Stacking ensemble strategies, XGBoost

CLC Number: 

  • TP311
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