A Novel Unweighted Combination Method for Business Failure Prediction Using Soft Set


Wei Xu, Daoli Yang, Journal of Information Processing Systems Vol. 15, No. 6, pp. 1489-1502, Dec. 2019  

10.3745/JIPS.04.0154
Keywords: Business Failure Prediction, Combination Method, Different Sample Sizes, Soft Set, uni-int Decision Making Method
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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.


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Cite this article
[APA Style]
Wei Xu and Daoli Yang (2019). A Novel Unweighted Combination Method for Business Failure Prediction Using Soft Set. Journal of Information Processing Systems, 15(6), 1489-1502. DOI: 10.3745/JIPS.04.0154.

[IEEE Style]
W. Xu and D. Yang, "A Novel Unweighted Combination Method for Business Failure Prediction Using Soft Set," Journal of Information Processing Systems, vol. 15, no. 6, pp. 1489-1502, 2019. DOI: 10.3745/JIPS.04.0154.

[ACM Style]
Wei Xu and Daoli Yang. 2019. A Novel Unweighted Combination Method for Business Failure Prediction Using Soft Set. Journal of Information Processing Systems, 15, 6, (2019), 1489-1502. DOI: 10.3745/JIPS.04.0154.