Credit Scoring Model based on Kernel Density Estimation and Support Vector Machine for Group Feature Selection

2018 ◽  
Author(s):  
Xingzhi Zhang ◽  
Zhurong Zhou
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Density Estimation ◽  
Kernel Density Estimation ◽  
Credit Scoring ◽  
Kernel Density ◽  
Support Vector ◽  
Scoring Model ◽  
Model Based ◽  
Credit Scoring Model

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

2019 ◽  
Vol 35 (2) ◽  
pp. 371-394 ◽  
Author(s):  
Diwakar Tripathi ◽  
Damodar Reddy Edla ◽  
Ramalingaswamy Cheruku ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Feature Selection ◽  
Credit Scoring ◽  
Ensemble Classification ◽  
Scoring Model ◽  
Model Based ◽  
Credit Scoring Model

Multi-Class Support Vector Machine for Credit Scoring

2012 ◽  
Vol 235 ◽  
pp. 419-422 ◽  
Author(s):  
Bo Tang ◽  
Sai Bing Qiu
Keyword(s):  
Support Vector Machine ◽  
Credit Scoring ◽  
Real Life ◽  
Assessment Model ◽  
Behavior Assessment ◽  
Support Vector ◽  
Classification Problems ◽  
Scoring Model ◽  
Multiple Classification ◽  
Credit Scoring Model

The general credit scoring model is to solve the two classification problems, but in real life we often encounter multiple classification problems. This paper proposes a multi-class support vector machine, which can solve multiple classification problems in the behavior assessment model.

scholarly journals Feature Selection in a Credit Scoring Model

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 746
Author(s):  
Juan Laborda ◽  
Seyong Ryoo
Keyword(s):  
Feature Selection ◽  
Credit Scoring ◽  
Superior Performance ◽  
Filter Method ◽  
Support Vector ◽  
Classification Algorithms ◽  
Scoring Model ◽  
Stepwise Selection ◽  
Forward Stepwise ◽  
Credit Scoring Model

This paper proposes different classification algorithms—logistic regression, support vector machine, K-nearest neighbors, and random forest—in order to identify which candidates are likely to default for a credit scoring model. Three different feature selection methods are used in order to mitigate the overfitting in the curse of dimensionality of these classification algorithms: one filter method (Chi-squared test and correlation coefficients) and two wrapper methods (forward stepwise selection and backward stepwise selection). The performances of these three methods are discussed using two measures, the mean absolute error and the number of selected features. The methodology is applied for a valuable database of Taiwan. The results suggest that forward stepwise selection yields superior performance in each one of the classification algorithms used. The conclusions obtained are related to those in the literature, and their managerial implications are analyzed.

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