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Credit scoring with a feature selection approach based deep learning

Van-Sang Ha, Ha-Nam Nguyen, K. Abou-El-Hossein
2016 MATEC Web of Conferences  
better performance than the feature selection methods widely used in credit scoring.  ...  Two public datasets, Australia and German credit ones, have been used to test our method.  ...  Many methods have been investigated in the last decade to pursue even small improvement in credit scoring accuracy.  ... 
doi:10.1051/matecconf/20165405004 fatcat:zssvh5vrkfcrzmxgy7al3seiay

Comparison of feature selection approaches based on the SVM classification

F.C. Li, F.L. Chen, G.E. Wang
2008 2008 IEEE International Conference on Industrial Engineering and Engineering Management  
Different features preprocessing steps were constructed with four strategies of conventional Linear discriminate analysis (LDA), Decision tree, Rough set and F-score models to optimize feature space by  ...  Our results suggest that hybrid credit scoring models can mostly classify the applicants as either good or bad clients that are robust and effective in finding optimal subsets and are a promising method  ...  Most credit scoring models have been widely developed by reducing redundant features to improve the accuracy of credit scoring models during the past few years.  ... 
doi:10.1109/ieem.2008.4737899 fatcat:f7j4ashapbbnbb4r6hoj46tdtq

Credit scoring with a data mining approach based on support vector machines

Cheng-Lung Huang, Mu-Chen Chen, Chieh-Jen Wang
2007 Expert systems with applications  
Experimental results show that SVM is a promising addition to the existing data mining methods.  ...  This study used three strategies to construct the hybrid SVM-based credit scoring models to evaluate the applicant's credit score from the applicant's input features.  ...  A simple thresholding scheme is sufficient for the BPN and GP to divide the feature space into two categories in a two-class classification problem.  ... 
doi:10.1016/j.eswa.2006.07.007 fatcat:nbgsgexi5ndapprucep23qedhe

Orthogonal support vector machine for credit scoring

Lu Han, Liyan Han, Hongwei Zhao
2013 Engineering applications of artificial intelligence  
The most commonly used techniques for credit scoring is logistic regression, and more recent research has proposed that the support vector machine is a more effective method.  ...  In this paper, we introduce a new way to address this problem which is defined as orthogonal dimension reduction.  ...  It is a method to evaluate the credit risk of loan applicants with their corresponding credit score that is obtained from a credit scoring model (Altman, 1998) .  ... 
doi:10.1016/j.engappai.2012.10.005 fatcat:2ftey756lfdw5jrtlrsllpreii

Weight-Selected Attribute Bagging for Credit Scoring

Jianwu Li, Haizhou Wei, Wangli Hao
2013 Mathematical Problems in Engineering  
In this paper, we propose an improved attribute bagging method, weight-selected attribute bagging (WSAB), to evaluate credit risk.  ...  Experimental results based on two credit benchmark databases show that the proposed method, WSAB, is outstanding in both prediction accuracy and stability, as compared to analogous methods.  ...  Acknowledgments The authors would like to thank the anonymous reviewers for their constructive comments and suggestions which have led to great improvement on this paper.  ... 
doi:10.1155/2013/379690 fatcat:lfgcklp74rdpvmypleywar3y7q

Filter- versus wrapper-based feature selection for credit scoring

Petr Somol, Bart Baesens, Pavel Pudil, Jan Vanthienen
2005 International Journal of Intelligent Systems  
We address the problem of credit scoring as a classification and feature subset selection problem.  ...  The feature selection methods are validated on several real world datasets with different types of classifiers. We show the advantages following from using the sub-space approach to classification.  ...  In this paper, we intend to investigate the potential of feature selection methods for credit scoring.  ... 
doi:10.1002/int.20103 fatcat:o5a7nvcb7vhqhhnekf5nkph5q4

XGBoost Optimized by Adaptive Particle Swarm Optimization for Credit Scoring

Chao Qin, Yunfeng Zhang, Fangxun Bao, Caiming Zhang, Peide Liu, Peipei Liu, Sotiris B. Kotsiantis
2021 Mathematical Problems in Engineering  
Personal credit scoring is a challenging issue. In recent years, research has shown that machine learning has satisfactory performance in credit scoring.  ...  Because of the advantages of feature combination and feature selection, decision trees can match credit data which have high dimension and a complex correlation.  ...  Fonseca [5] proposed a two-stage process that employs a fuzzy inference model as an input for an NN model using a credit score rating as a response to conduct the fuzzy reasoning step of the analysis  ... 
doi:10.1155/2021/6655510 fatcat:64wvmu7kgjgv3k3hrrq2a5pfe4

Does Feature Reduction Help Improve the Classification Accuracy Rates? A Credit Scoring Case Using a German Data Set

Jozef Zurada
2010 Review of Business Information Systems (RBIS)  
A preliminary computer simulation performed on a German data set drawn from the credit scoring context shows mixed results.  ...  In general, these activities improve the performance of predictive models. In particular, the paper investigates the effect of feature reduction on classification accuracy rates.  ...  Attribute noise can be filtered by transforming to the PC space, eliminating some of the worst eigenvectors, and then transforming back to the original space.  ... 
doi:10.19030/rbis.v14i2.496 fatcat:qgapsfw3v5b4zc5hw75l4iznqm

HETEROSCEDASTIC DISCRIMINANT ANALYSIS COMBINED WITH FEATURE SELECTION FOR CREDIT SCORING

Katarzyna Stąpor, Tomasz Smolarczyk, Piotr Fabian
2016 Statistics in Transition New Series  
To evaluate the accuracy of the proposed credit scoring model and to compare it with the existing approaches we have used the German credit data set from the study (Chen, Li, 2010) .  ...  We have tested five feature subset selection algorithms: two filters and three wrappers.  ...  Many credit scoring models have been widely developed by reducing redundant features through feature selection to improve the accuracy of credit scoring models during the past few years.  ... 
doi:10.21307/stattrans-2016-018 fatcat:n2wlx5dbdvelzhmhhjnxpsrmue

Large-scale Uncertainty Estimation and Its Application in Revenue Forecast of SMEs [article]

Zebang Zhang, Kui Zhao, Kai Huang, Quanhui Jia, Yanming Fang, Quan Yu
2020 arXiv   pre-print
Business credit loans are very important for the operation of SMEs, and the revenue is a key indicator of credit limit management.  ...  Therefore, it is very beneficial to construct a reliable revenue forecasting model.  ...  Such features may not improve the point estimation performance but may improve the accuracy of the distribution estimation.  ... 
arXiv:2005.00718v1 fatcat:josy24qxxfbyrkft2icilygnpy

Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features [article]

Rory P. Bunker, Wenjun Zhang, M. Asif Naeem
2017 arXiv   pre-print
in improving the credit scoring model.  ...  Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously.  ...  In a separate line, data from bank statements obtained through applications like Credit Sense could be used in investigating possible fraud cases, for example, by using methods of outlier detection.  ... 
arXiv:1611.00252v2 fatcat:y3kfz2i3kzcinbwb2tvn3rovly

Selection Features and Support Vector Machine for Credit Card Risk Identification

Naoufal Rtayli, Nourddine Enneya
2020 Procedia Manufacturing  
RFC has a good performance; it tends to identify the most predictive features, which may provide a significant improvement in classification performance of credit card risk identification model.  ...  RFC has a good performance; it tends to identify the most predictive features, which may provide a significant improvement in classification performance of credit card risk identification model.  ...  In this regard, we propose our model that is formed of two steps:  Step1: Selection of relevant features based on Random Forest Classifier Method in order to increase the performance of the model.  ... 
doi:10.1016/j.promfg.2020.05.012 fatcat:ry7sofca6bb65hqto23vaia2wy

A Multi-stage Self-adaptive Classifier Ensemble Model with Application in Credit Scoring

Shanshan Guo, Hongliang He, Xiaoling Huang
2019 IEEE Access  
The proposed model is applied to credit scoring to test its prediction performance.  ...  This paper presents a novel multi-stage self-adaptive classifier ensemble model based on the statistical techniques and the machine learning techniques to improve the prediction performance.  ...  Zhou et al. use direct search method to optimize the SVM-based credit scoring model, and the experimental results show that the performance and robustness of SVM is improved after this method [21] .  ... 
doi:10.1109/access.2019.2922676 fatcat:uuw4vczo3rfpnnxgfilc7f4w4m

Incremental Feature Learning For Infinite Data [article]

Armin Sadreddin, Samira Sadaoui
2021 arXiv   pre-print
We introduce a new adaptive learning approach that adjusts frequently and efficiently to new transaction chunks; each chunk is discarded after each incremental training step.  ...  The former improves the feature relevancy for subsequent chunks, and the latter, a new paradigm, increases accuracy during training by determining the optimal network architecture dynamically for each  ...  The 2-class dataset possesses 30 predictive features obtained with the feature extraction method PCA; the two features Time and Amount were not transformed as their actual values are essential.  ... 
arXiv:2108.02932v1 fatcat:zbaugegm4jcanbigtplsr5dbdm

The Image Classification Method with CNN-XGBoost Model Based on Adaptive Particle Swarm Optimization

Wenjiang Jiao, Xingwei Hao, Chao Qin
2021 Information  
Moreover, the APSO-XGBoost model performs well on the credit data, which indicates that the model has a good ability of credit scoring.  ...  in space by two different strategies, which improves the diversity of particle population and prevents the algorithm from becoming trapped in local optima.  ...  a regression or classification tree.  ... 
doi:10.3390/info12040156 fatcat:7rp7wfmcv5dj3bd25zs4nlhcu4
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