A Financial Distress Prediction Model Based on Sparse Algorithm and Support Vector Machine

Table 5

Classification results of sparse principal component SVM.

Dataset I
	Accuracy	Precision	Recall	F1
OF-SVM	72.22	80.77	58.33	67.74
PCA-SVM	75.00	78.13	72.50	76.32
LDA-KNN	55.56	54.55	66.67	60.00
KPCA-SVM	48.61	49.15	80.56	61.05
KDA-KNN	58.33	57.89	61.11	59.46
SPCA-SVM	68.06	72.41	58.33	64.62
GSPCA-SVM	81.94	81.08	83.33	82.19
Dataset II
	Accuracy	Precision	Recall	F1
OF-SVM	76.27	94.29	55.93	70.21
PCA-SVM	77.96	94.59	59.32	72.92
LDA-KNN	55.08	55.77	49.15	52.25
KPCA-SVM	48.30	49.00	83.05	61.64
KDA-KNN	55.93	56.14	54.24	55.17
SPCA-SVM	62.71	75.86	37.29	50.00
GSPCA-SVM	79.66	92.68	64.41	76.00