Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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A Novel Unweighted Combination Method for Business Failure Prediction Using Soft Set

  • Xu, Wei (School of Business, Jiangnan University) ;
  • Yang, Daoli (School of Business Planning, Chongqing Technology and Business University)
  • Received : 2018.04.05
  • Accepted : 2019.06.20
  • Published : 2019.12.31
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Abstract

This work introduces a novel unweighted combination method (UCSS) for business failure perdition (BFP). With considering features of BFP in the age of big data, UCSS integrates the quantitative and qualitative analysis by utilizing soft set theory (SS). We adopt the conventional expert system (ES) as the basic qualitative classifier, the logistic regression model (LR) and the support vector machine (SVM) as basic quantitative classifiers. Unlike other traditional combination methods, we employ soft set theory to integrate the results of each basic classifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify the performance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing the equal weight approach (CMEW), neural network algorithm (CMNN), rough set and D-S evidence theory (CMRD), and the receiver operating characteristic curve (ROC) and SS (CFBSS) as benchmarks. The superior performance of UCSS has been verified by the empirical experiments.

Keywords

Acknowledgement

This study was supported by the National Natural Science Foundation of China (No. 71801113, 71602077), the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 18YJC630212), and Fundamental Research Funds for the Central Universities (No. JUSRP11764).

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