Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm

Table 3

Classification results of different sample matching methods.

Dataset	Data set I					Data set II
Method	Cr	F1	G		MCC	Cr	F1	G	MCC
RO	87.55	86.24	87.03		76.51	85.92	84.13	85.17	73.73
SMOTE	86.95	85.94	86.65		74.70	85.21	83.46	84.55	72.05
ADASYN	87.68	86.82	87.44		76.01	84.51	82.54	83.75	70.84
RU	89.85	89.19	89.63		80.32	87.68	86.36	87.14	76.80
NearMiss	77.54	80.25	76.30		57.29	77.46	79.22	77.00	55.73
S-KFCM	94.20	94.03	94.16		88.55	88.03	87.02	87.69	76.99
DATASET	Data set III					Data set IV
Method	CR	F1	G	MCC		CR	F1	G	MCC
RO	81.25	78.56	80.19	63.60		85.23	83.54	84.61	71.98
SMOTE	81.25	78.76	80.40	64.29		83.87	81.88	83.15	69.43
ADASYN	80.86	78.22	79.95	63.61		83.55	81.59	82.87	68.67
RU	81.91	79.51	81.06	65.66		85.20	83.52	84.58	71.94
NearMiss	76.95	77.74	76.87	54.04		77.74	78.77	77.59	55.75
S-KFCM	83.98	81.78	83.11	70.05		86.45	85.00	85.91	74.31

The bold values are the maximum of the list.