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

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  • Baofeng Shi
  • Bin Meng
  • Hufeng Yang
  • Jing Wang
  • Wenli Shi

Abstract

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.

Suggested Citation

  • Baofeng Shi & Bin Meng & Hufeng Yang & Jing Wang & Wenli Shi, 2018. "A Novel Approach for Reducing Attributes and Its Application to Small Enterprise Financing Ability Evaluation," Complexity, Hindawi, vol. 2018, pages 1-17, January.
  • Handle: RePEc:hin:complx:1032643
    DOI: 10.1155/2018/1032643
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    References listed on IDEAS

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    1. Chen, Guo & Dong, Zhao Yang & Hill, David J. & Zhang, Guo Hua & Hua, Ke Qian, 2010. "Attack structural vulnerability of power grids: A hybrid approach based on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(3), pages 595-603.
    2. Baofeng Shi & Hufeng Yang & Jing Wang & Jingxu Zhao, 2016. "City Green Economy Evaluation: Empirical Evidence from 15 Sub-Provincial Cities in China," Sustainability, MDPI, vol. 8(6), pages 1-39, June.
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    Cited by:

    1. Shi, Baofeng & Zhao, Xue & Wu, Bi & Dong, Yizhe, 2019. "Credit rating and microfinance lending decisions based on loss given default (LGD)," Finance Research Letters, Elsevier, vol. 30(C), pages 124-129.
    2. Bai, Chunguang & Shi, Baofeng & Liu, Feng & Sarkis, Joseph, 2019. "Banking credit worthiness: Evaluating the complex relationships," Omega, Elsevier, vol. 83(C), pages 26-38.
    3. Shi, Baofeng & Chi, Guotai & Li, Weiping, 2020. "Exploring the mismatch between credit ratings and loss-given-default: A credit risk approach," Economic Modelling, Elsevier, vol. 85(C), pages 420-428.
    4. Yingli Wu & Guangji Tong, 2022. "The evaluation of agricultural enterprise's innovative borrowing capacity based on deep learning and BP neural network," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1111-1123, December.
    5. Sun, Yue & Chai, Nana & Dong, Yizhe & Shi, Baofeng, 2022. "Assessing and predicting small industrial enterprises’ credit ratings: A fuzzy decision-making approach," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1158-1172.
    6. Yuan, Kunpeng & Chi, Guotai & Zhou, Ying & Yin, Hailei, 2022. "A novel two-stage hybrid default prediction model with k-means clustering and support vector domain description," Research in International Business and Finance, Elsevier, vol. 59(C).

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