A Novel Unweighted Combination Method for Business Failure Prediction Using Soft Set
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초록
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 analysisby 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 basicclassifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify theperformance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing theequal 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 superiorperformance of UCSS has been verified by the empirical experiments.
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 analysisby 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 basicclassifier without weighting. In this way, UCSS inherits the advantages of ES, LR, SVM, and SS. To verify theperformance of UCSS, it is applied to real datasets. We adopt ES, LR, SVM, combination models utilizing theequal 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 superiorperformance of UCSS has been verified by the empirical experiments.
참고문헌 (25)
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