Abstract
In present days, prediction of financial crisis of a company is a hot research area. The use of data mining and machine learning algorithms assists to resolve the financial crisis prediction (FCP) problem. Since financial data contain more demographical and unwanted information, it might decrease the classification performance significantly. So, feature selection (FS) process is applied to choose useful data and remove the irrelevant repetitive data. This paper introduces a novel predictive framework for FCP model by the incorporation of improved grey wolf optimization (IGWO) and fuzzy neural classifier (FNC). An IGWO algorithm is derived by the integration of GWO algorithm and tumbling effect. The presented IGWO-based FS method is employed to discover the optimal features from the financial data. For classification purposes, FNC is employed. The proposed method is experimented on two benchmark data sets, namely Australian Credit and German data set under several of performance metrics. The experimental values verified the superior nature of the proposed FCP model over the compared methods.
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Communicated by O. Castillo, D. K. Jana.
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Sankhwar, S., Gupta, D., Ramya, K.C. et al. Improved grey wolf optimization-based feature subset selection with fuzzy neural classifier for financial crisis prediction. Soft Comput 24, 101–110 (2020). https://doi.org/10.1007/s00500-019-04323-6
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DOI: https://doi.org/10.1007/s00500-019-04323-6