Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering
The micro enterprises’ credit indicators with credit identification ability are selected by the two classification models of Support Vector Machine for the first round of indicator selection and then for the second round of indicator selection, deleting credit indicators with redundant information b...
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Hindawi Limited
2018-01-01
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Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2018/6390720 |
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doaj-71731659caef42718623fe7692862adc2020-11-24T21:44:24ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/63907206390720Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type ClusteringZhanjiang Li0Chengrong Yang1College of Economics and Management, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot, ChinaCollege of Economics and Management, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot, ChinaThe micro enterprises’ credit indicators with credit identification ability are selected by the two classification models of Support Vector Machine for the first round of indicator selection and then for the second round of indicator selection, deleting credit indicators with redundant information by clustering variables through the principle of minimum sum of deviation squares. This paper provides a screening model for credit evaluation indicators of micro enterprises and uses credit data of 860 micro enterprises samples in Inner Mongolia in western China for application analysis. The test results show that, first, the constructed final micro enterprises’ credit indicator system is in line with the 5C model; second, the validity test based on the ROC (Receiver Operating Characteristic) curve reveals that each of the screened credit evaluation indicators is valid.http://dx.doi.org/10.1155/2018/6390720 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhanjiang Li Chengrong Yang |
spellingShingle |
Zhanjiang Li Chengrong Yang Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering Mathematical Problems in Engineering |
author_facet |
Zhanjiang Li Chengrong Yang |
author_sort |
Zhanjiang Li |
title |
Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering |
title_short |
Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering |
title_full |
Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering |
title_fullStr |
Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering |
title_full_unstemmed |
Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering |
title_sort |
establishment of the credit indicator system of micro enterprises based on support vector machine and r-type clustering |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
The micro enterprises’ credit indicators with credit identification ability are selected by the two classification models of Support Vector Machine for the first round of indicator selection and then for the second round of indicator selection, deleting credit indicators with redundant information by clustering variables through the principle of minimum sum of deviation squares. This paper provides a screening model for credit evaluation indicators of micro enterprises and uses credit data of 860 micro enterprises samples in Inner Mongolia in western China for application analysis. The test results show that, first, the constructed final micro enterprises’ credit indicator system is in line with the 5C model; second, the validity test based on the ROC (Receiver Operating Characteristic) curve reveals that each of the screened credit evaluation indicators is valid. |
url |
http://dx.doi.org/10.1155/2018/6390720 |
work_keys_str_mv |
AT zhanjiangli establishmentofthecreditindicatorsystemofmicroenterprisesbasedonsupportvectormachineandrtypeclustering AT chengrongyang establishmentofthecreditindicatorsystemofmicroenterprisesbasedonsupportvectormachineandrtypeclustering |
_version_ |
1725910611450134528 |