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Mathematical Problems in Engineering
Volume 2018, Article ID 6390720, 11 pages
https://doi.org/10.1155/2018/6390720
Research Article

Establishment of the Credit Indicator System of Micro Enterprises Based on Support Vector Machine and R-Type Clustering

College of Economics and Management, Inner Mongolia Agricultural University, No. 306 Zhaowuda Road, Saihan District, Hohhot, China

Correspondence should be addressed to Zhanjiang Li; moc.361@285gnaijnahzil

Received 17 August 2017; Revised 6 February 2018; Accepted 20 February 2018; Published 8 April 2018

Academic Editor: Marco Mussetta

Copyright © 2018 Zhanjiang Li and Chengrong Yang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.