A Transfer Learning Based Classifier Ensemble Model for Customer Credit Scoring

2014 ◽  
Author(s):  
Jin Xiao ◽  
Runzhe Wang ◽  
Geer Teng ◽  
Yi Hu
Keyword(s):  
Transfer Learning ◽  
Credit Scoring ◽  
Classifier Ensemble ◽  
Ensemble Model

scholarly journals One-Step Dynamic Classifier Ensemble Model for Customer Value Segmentation with Missing Values

2014 ◽  
Vol 2014 ◽  
pp. 1-15
Author(s):  
Jin Xiao ◽  
Bing Zhu ◽  
Geer Teng ◽  
Changzheng He ◽  
Dunhu Liu
Keyword(s):  
Customer Value ◽  
Missing Values ◽  
Credit Scoring ◽  
Customer Relationship ◽  
Classifier Ensemble ◽  
Ensemble Model ◽  
Churn Prediction ◽  
Customer Data ◽  
Customer Churn ◽  
One Step

Scientific customer value segmentation (CVS) is the base of efficient customer relationship management, and customer credit scoring, fraud detection, and churn prediction all belong to CVS. In real CVS, the customer data usually include lots of missing values, which may affect the performance of CVS model greatly. This study proposes a one-step dynamic classifier ensemble model for missing values (ODCEM) model. On the one hand, ODCEM integrates the preprocess of missing values and the classification modeling into one step; on the other hand, it utilizes multiple classifiers ensemble technology in constructing the classification models. The empirical results in credit scoring dataset “German” from UCI and the real customer churn prediction dataset “China churn” show that the ODCEM outperforms four commonly used “two-step” models and the ensemble based model LMF and can provide better decision support for market managers.

scholarly journals A Multi-Stage Self-Adaptive Classifier Ensemble Model With Application in Credit Scoring

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 78549-78559 ◽  
Author(s):  
Shanshan Guo ◽  
Hongliang He ◽  
Xiaoling Huang
Keyword(s):  
Credit Scoring ◽  
Classifier Ensemble ◽  
Ensemble Model ◽  
Multi Stage ◽  
Self Adaptive

scholarly journals A clustering and selection based transfer ensemble model for customer credit scoring

Filomat ◽  
2016 ◽  
Vol 30 (15) ◽  
pp. 4015-4026
Author(s):  
Jin Xiao ◽  
Ling Xie ◽  
Dunhu Liu ◽  
Yi Xiao ◽  
Yi Hu
Keyword(s):  
Transfer Learning ◽  
Ensemble Learning ◽  
Test Data ◽  
Credit Scoring ◽  
Experimental Results ◽  
Ensemble Model ◽  
Learning Models ◽  
Target Domain ◽  
Satisfactory Performance ◽  
Important Concern

Customer credit scoring is an important concern for numerous domestic and global industries. It is difficult to achieve satisfactory performance by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study combines ensemble learning with transfer learning, and proposes a clustering and selection based transfer ensemble (CSTS) model to transfer the instances from related source domains to target domain for assisting in modeling. The experimental results in two customer credit scoring datasets show that CSTE model outperforms two traditional credit scoring models, as well as three existing transfer learning models.

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

2021 ◽  
Vol 40 (5) ◽  
pp. 9471-9484
Author(s):  
Yilun Jin ◽  
Yanan Liu ◽  
Wenyu Zhang ◽  
Shuai Zhang ◽  
Yu Lou
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Imbalanced Data ◽  
Ensemble Model ◽  
Selective Sampling ◽  
Machine Learning Methods ◽  
Multi Stage ◽  
Proposed Model ◽  
New Feature

With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a new feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

A novel multi-stage ensemble model for credit scoring based on synthetic sampling and feature transformation

2021 ◽  
pp. 1-16
Author(s):  
Fang He ◽  
Wenyu Zhang ◽  
Zhijia Yan
Keyword(s):  
Credit Scoring ◽  
Imbalanced Data ◽  
Transformation Method ◽  
Classification Performance ◽  
Ensemble Model ◽  
Feature Transformation ◽  
Learning Methods ◽  
Scoring Model ◽  
Multi Stage ◽  
Credit Scoring Model

Credit scoring has become increasingly important for financial institutions. With the advancement of artificial intelligence, machine learning methods, especially ensemble learning methods, have become increasingly popular for credit scoring. However, the problems of imbalanced data distribution and underutilized feature information have not been well addressed sufficiently. To make the credit scoring model more adaptable to imbalanced datasets, the original model-based synthetic sampling method is extended herein to balance the datasets by generating appropriate minority samples to alleviate class overlap. To enable the credit scoring model to extract inherent correlations from features, a new bagging-based feature transformation method is proposed, which transforms features using a tree-based algorithm and selects features using the chi-square statistic. Furthermore, a two-layer ensemble method that combines the advantages of dynamic ensemble selection and stacking is proposed to improve the classification performance of the proposed multi-stage ensemble model. Finally, four standardized datasets are used to evaluate the performance of the proposed ensemble model using six evaluation metrics. The experimental results confirm that the proposed ensemble model is effective in improving classification performance and is superior to other benchmark models.

A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring

2021 ◽  
Vol 165 ◽  
pp. 113872
Author(s):  
Wenyu Zhang ◽  
Dongqi Yang ◽  
Shuai Zhang ◽  
Jose H. Ablanedo-Rosas ◽  
Xin Wu ◽  
...  
Keyword(s):  
Credit Scoring ◽  
Ensemble Model ◽  
Multi Stage

An Ensemble Learning Model Based on SOM-SVM Model for Personal Credit Risk

2011 ◽  
Vol 271-273 ◽  
pp. 1286-1290
Author(s):  
Yan Feng Guo ◽  
Na Sun ◽  
Yuan Yao
Keyword(s):  
Credit Risk ◽  
Financial Management ◽  
Financial Risk ◽  
Credit Scoring ◽  
Experimental Result ◽  
Ensemble Model ◽  
Single Model ◽  
Personal Credit ◽  
Svm Model ◽  
Management Area

Credit risk problem is an essential problem in financial management area. People usually employ personal credit scoring to avoid financial risk problem. Although many methods have been proposed for evaluating the personal credit scoring and obtained good effects, most of these methods were called single model types, which would be disturbed by model self-parameter, data noise and other external factors. In order to overcome the weakness of single model, we believe one of best ways is to construct an ensemble model. In this paper, we proposed a new style of ensemble model and employed two public credit datasets to certify the validity of our ensemble model. The experimental result shows that the ensemble SOM-SVM model can overcome the single model weakness and improve the accuracy of classification, which is good for constructing a better credit scoring system in future.

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