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Volume 2018 (2018), Article ID 1032643, 17 pages
Research Article

A Novel Approach for Reducing Attributes and Its Application to Small Enterprise Financing Ability Evaluation

1College of Economics & Management, Northwest A&F University, Yangling, Shaanxi 712100, China
2Collaborative Innovation Center for Transport Studies, Dalian Maritime University, Dalian, Liaoning 116026, China
3School of Management Engineering and Business, Hebei University of Engineering, Handan, Hebei 056038, China

Correspondence should be addressed to Wenli Shi

Received 28 June 2017; Accepted 5 December 2017; Published 15 January 2018

Academic Editor: Dimitri Volchenkov

Copyright © 2018 Baofeng Shi et al. 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.


Attribute reduction is viewed as a kind of preprocessing steps for reducing large dimensionality in data mining of all complex systems. A great deal of researchers have proposed various approaches to reduce attributes or select key features in multicriteria decision making evaluation. In practice, the existing approaches for attribute reduction focused on improving the classification accuracy or saving the cost of computational time, without considering the influence of the reduction results on the original data set. To help address this gap, we develop an advanced novel attribute reduction approach combining Pearson correlation analysis with test significance discrimination for the screening and identification of key characteristics related to the original data set. The proposed model has been verified using the financing ability evaluation data of 713 small enterprises of a city commercial bank in China. And the experimental results show that the proposed reduction model is efficient and effective. Moreover, our experimental findings help to locate the qualified partners and alleviate the difficulties faced by enterprises when applying loan.