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

Table 5

Classification results of different classifier algorithms.

Dataset	Data set I				Data set II
Method	Cr	F1	G	MCC	Cr	F1	G	MCC
MLFNN	74.64	79.77	70.20	57.18	88.03	86.82	87.55	77.36
KNN	86.23	84.55	85.54	74.24	81.69	78.69	80.47	66.06
NB	50.72	66.34	20.55	3.880	85.93	84.13	85.17	73.74
C4.5	65.22	59.32	63.59	31.80	90.85	90.91	90.84	81.70
Bagging	80.43	79.39	80.27	61.19	79.58	80.27	79.50	59.30
SVM	94.93	94.81	94.90	89.94	90.14	89.23	89.74	81.45
DATASET	Data set III				Data set IV
Method	CR	F1	G	MCC	CR	F1	G	MCC
MLFNN	79.30	74.64	77.14	62.99	79.03	80.24	78.79	58.51
KNN	78.52	75.77	77.69	58.55	76.45	72.45	75.06	55.28
NB	82.03	79.82	81.30	65.65	76.13	77.02	76.03	52.42
C4.5	82.03	82.17	81.25	64.07	68.06	58.58	64.10	40.64
Bagging	80.08	79.35	80.00	60.31	84.19	84.04	84.19	68.40
SVM	83.98	82.10	83.32	69.53	87.10	85.82	86.63	75.43

The bold values are the maximum of the list.