Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study

Authors:
Liang-Hsuan Chen;Huey-Der Hsiao
Affiliations:
Department of Industrial and Information Management, National Cheng Kung University, Tainan 701, Taiwan, ROC;Department of Industrial and Information Management, National Cheng Kung University, Tainan 701, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2008

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Abstract

This research is aimed at establishing the diagnosis models for business crises through integrating a real-valued genetic algorithm to determine the optimum parameters and SVM to perform learning and classification on data. After finishing the training processes, the proposed GA-SVM can reach a prediction accuracy of up to 95.56% for all the tested business data. Particularly, only six influential features are included in the proposed model with intellectual capital and financial features after the 2-phase selecting process; the six features are ordinary and widely available from public business reports. The proposed GA-SVM is available for business managers to conduct self-diagnosis in order to realize whether business units are really facing a crisis.