Management decision by combination two-level DEA and kernel-based mechanism

This study proposed a novel hybrid mechanism for corporate operating assessment and then establishes a fusion architecture that integrates two-level data envelopment analysis (two-level DEA), incremental filter wrapper feature selection technique (IFWFS), and fuzzy support vector machine (FSVM) for performance forecasting. The two-level DEA is executed to deal with the problem encountered by one-level DEA (i.e., original DEA) and further enhances its discriminant ability. Sequentially, the IFWFS is applied to determine the most essential feature subsets as well as decreases the calculation complexities and storage requirements. The selected important features were fed into FSVM (one of the kernel-based mechanisms) to construct the forecasting model. The proposed model has been examined by real-life cases and is a promising alternative for corporate risk management. The decision makers can take this mechanism as a decision support tool to modify their investment portfolios as well as increase their profit margin under this fluctuating business atmosphere.

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