A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Tripathi, Diwakar; Edla, Damodar Reddy; Cheruku, Ramalingaswamy; Kuppili, Venkatanareshbabu

doi:10.1111/coin.12200

Cited by 51 publications

(24 citation statements)

References 65 publications

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“…To solve this problem, more and more researchers have studied feature selection in recent years. Previous studies have shown that a single feature selection method cannot handle all classifiers and data sets well [ 32 ]. Although the existing studies have begun to focus on combining multiple feature selection methods to improve the performance of the classifier, there is a lack of interpretable optimal feature set determination method analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Liu

et al. 2021

Entropy

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Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusion: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.

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Section: Literature Reviewmentioning

confidence: 99%

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Liu

et al. 2021

Entropy

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“…[28] found the problem of misclassifications near the optimal hyper plane by adopting SVM, accordingly provided an SVM-KNN Hybrid model with K-Nearest Neighbor (KNN) to cope with the defects, and validated this improved method on CRE. [29] proposed a three-phase hybrid credit prediction model, which contains preprocessing, ensemble feature selection and multilayer classifier framework. [30] presented a feature selection-based Hybrid-bagging algorithm (FS-HB) to assess credit risk, and obtained better performance compared with feature selection-based classifier and bagging.…”

Section: A Dbm-drbm Hybrid Modelmentioning

confidence: 99%

“…In the experiment, we used the Lending Club dataset. The official loan status of the dataset contains 6 categories, which is 'current' 'fully paid' 'late (16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)' 'in grace period' 'late (31-120)' 'charged off'. For the purpose of this study, we considered loans issued of whole year of 2018, filtering out loans that are not fully paid or charged off yet.…”

Section: { }mentioning

confidence: 99%

A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology

Wu¹

2021

IJMLC

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Credit risk evaluation (CRE) is a very challenging and important management science problem in the domain of financial analysis. Many popular methods have been applied to tackle this problem in recent years. However, feature extraction and imbalanced data problem have not been paid enough attention in the current research, which play significant function in field of CRE. In this paper, we employed a deep learning approach to extract effective features and under-sampling technique to balance dataset. Our model combine under-sampling technique, Deep Boltzmann Machine (DBM) and Discriminative Restricted Boltzmann Machine (DRBM) method. To examine the performance, real world credit data of Lending Club is applied in the proposed model. The stable and better performance results show that the Hybrid classifier we propose is more effective and powerful.

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“…This study introduces a novel procedure related to the model estimation and feature selection for ANNs in the context of credit scoring. Basically, this procedure is hybridized training of ANNs with a novel feature selection approach based on genetic algorithms (GAs) and information complexity criterion (ICOMP) 74‐77 …”

Section: Introductionmentioning

confidence: 99%

Hybridized artificial neural network classifiers with a novel feature selection procedure based genetic algorithms and information complexity in credit scoring

Ilter

Deniz

Kocadağlı

2021

Appl Stoch Models Bus & Ind

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The credit scoring is a statistical analysis performed by financial institutions to represent the creditworthiness of an individual or small and medium‐sized enterprise. A credit score is a numerical quantity that can be qualified by a rating label showing the potential risk. In order to determine which customers are likely to bring in the most revenue at the exact interest rate and credit limits, the lenders take into accounts these credit scores. In this context, the robust statistical models are inevitable to reduce the number of wrong decisions in the credit evaluation process. Although the machine learning approaches provide superior performance to conventional statistical methods, they are mostly criticized due to the selection of model structure, model complexity, tuning parameters, time consumption in the high‐dimensional and excessive nonlinear cases. For this reason, this study introduces an efficient model estimation and feature selection procedure for artificial neural network (ANN) classifiers in the context of credit scoring. Essentially, this procedure hybridizes training of ANNs with a novel feature selection approach based on genetic algorithms and information complexity criterion. In the application, the proposed procedure was performed on a couple of benchmark credit scoring datasets. According to analysis results, the proposed approach not only estimates robust models from ANNs in terms of model complexity, feature selection, and time consumption, but also outperforms the traditional training procedure for the classification accuracies, false positive, and false negative errors overtraining and test datasets.

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A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

Cited by 51 publications

References 65 publications

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

A Credit Risk Predicting Hybrid Model Based on Deep Learning Technology

Hybridized artificial neural network classifiers with a novel feature selection procedure based genetic algorithms and information complexity in credit scoring

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