Loan default prediction using a credit rating-specific and multi-objective ensemble learning scheme

[1]  Vincenzo Moscato,et al.  A benchmark of machine learning approaches for credit score prediction , 2021, Expert Syst. Appl..

[2]  Sumit Singh Chauhan,et al.  A review on genetic algorithm: past, present, and future , 2020, Multim. Tools Appl..

[3]  Mark Goh,et al.  2-stage modified random forest model for credit risk assessment of P2P network lending to "Three Rurals" borrowers , 2020, Appl. Soft Comput..

[4]  Yan Liu,et al.  Resampling ensemble model based on data distribution for imbalanced credit risk evaluation in P2P lending , 2020, Inf. Sci..

[5]  Stefan Lessmann,et al.  Cost-sensitive business failure prediction when misclassification costs are uncertain: A heterogeneous ensemble selection approach , 2020, Eur. J. Oper. Res..

[6]  Sami Ben Jabeur,et al.  Machine learning models and cost-sensitive decision trees for bond rating prediction , 2020, J. Oper. Res. Soc..

[7]  Xin Ye,et al.  Multi-view ensemble learning based on distance-to-model and adaptive clustering for imbalanced credit risk assessment in P2P lending , 2020, Inf. Sci..

[8]  Gilberto Reynoso-Meza,et al.  Ensemble learning by means of a multi-objective optimization design approach for dealing with imbalanced data sets , 2020, Expert Syst. Appl..

[9]  Chunhui Zhao,et al.  A cluster-based hybrid sampling approach for imbalanced data classification. , 2020, The Review of scientific instruments.

[10]  Feng SHEN,et al.  A COST-SENSITIVE LOGISTIC REGRESSION CREDIT SCORING MODEL BASED ON MULTI-OBJECTIVE OPTIMIZATION APPROACH , 2019 .

[11]  Khaled Ghédira,et al.  Rule-based credit risk assessment model using multi-objective evolutionary algorithms , 2019, Expert Syst. Appl..

[12]  Alexandru Coser,et al.  Predictive Models for Loan Default Risk Assessment , 2019, ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH.

[13]  Rui Liu,et al.  Self-adaptive cost weights-based support vector machine cost-sensitive ensemble for imbalanced data classification , 2019, Inf. Sci..

[14]  Yaochu Jin,et al.  Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection , 2019, Appl. Soft Comput..

[15]  Richard A. Bauder,et al.  A survey on addressing high-class imbalance in big data , 2018, Journal of Big Data.

[16]  Licheng Jiao,et al.  Multiobjective sparse ensemble learning by means of evolutionary algorithms , 2018, Decis. Support Syst..

[17]  Shuai Zhang,et al.  A novel ensemble method for credit scoring: Adaption of different imbalance ratios , 2018, Expert Syst. Appl..

[18]  Bertrand K. Hassani,et al.  Credit Risk Analysis Using Machine and Deep Learning Models , 2018 .

[19]  A. Lapthorn,et al.  Evolutionary multi-objective fault diagnosis of power transformers , 2017, Swarm Evol. Comput..

[20]  Yufei Xia,et al.  Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending , 2017, Electron. Commer. Res. Appl..

[21]  Tobias Regner,et al.  Determinants of Borrowers' Default in P2P Lending under Consideration of the Loan Risk Class , 2016, Games.

[22]  Enhong Chen,et al.  Portfolio Selections in P2P Lending: A Multi-Objective Perspective , 2016, KDD.

[23]  Dan Simon,et al.  Ensemble multi-objective biogeography-based optimization with application to automated warehouse scheduling , 2015, Eng. Appl. Artif. Intell..

[24]  Lin Zhang,et al.  Two methods of selecting Gaussian kernel parameters for one-class SVM and their application to fault detection , 2014, Knowl. Based Syst..

[25]  Yashwant Prasad Singh,et al.  ONE-CLASS SUPPORT VECTOR MACHINES APPROACH TO ANOMALY DETECTION , 2013, Appl. Artif. Intell..

[26]  Nuno Vasconcelos,et al.  Cost-Sensitive Support Vector Machines , 2012, Neurocomputing.

[27]  Hong Gu,et al.  Anomaly detection combining one-class SVMs and particle swarm optimization algorithms , 2010 .

[28]  Detlef Seese,et al.  A hybrid heuristic approach to discrete multi-objective optimization of credit portfolios , 2004, Comput. Stat. Data Anal..

[29]  Gustavo E. A. P. A. Batista,et al.  A study of the behavior of several methods for balancing machine learning training data , 2004, SKDD.

[30]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[31]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[32]  Hamido Fujita,et al.  Imbalanced enterprise credit evaluation with DTE-SBD: Decision tree ensemble based on SMOTE and bagging with differentiated sampling rates , 2018, Inf. Sci..

[33]  B. Ribeiro,et al.  Financial credit risk assessment: a recent review , 2015, Artificial Intelligence Review.

[34]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..