SVM Combined with FCM and PCA for Financial Diagnosis

Financial diagnosis is an important and widely studied topic in the last three decades. Recently, the support vector machine (SVM) has been applied to the problem of financial diagnosis. Fuzzy c-means clustering (FCM) is among considerable techniques for data reduction. In addition, principal component analysis (PCA) is a powerful technique for feather extraction. This paper proposes using fuzzy c-means clustering algorithm, principle component analysis to make SVM more effective. The algorithm proposed in this paper, FCM-PCA-SVM composed of three subnetworks: fuzzy classifier, layer of feather extraction with principal component analysis and support vector machine. Empirical results using Chinese listed companies show that the hybrid model is very promising for financial diagnosis in terms of predictive accuracy.