Research Article
Research on Audit Opinion Prediction of Listed Companies Based on Sparse Principal Component Analysis and Kernel Fuzzy Clustering Algorithm
Table 4
Classification results of different characteristic degradation algorithms.
| Dataset | Data set I | Data set II | Method | Cr | F1 | G | MCC | Cr | F1 | G | MCC |
| LDA | 56.42 | 67.06 | 54.71 | 21.27 | 63.38 | 63.89 | 63.36 | 26.77 | PCA | 90.58 | 90.08 | 90.44 | 81.58 | 88.03 | 75.86 | 86.82 | 77.36 | SPCA | 93.48 | 93.23 | 93.41 | 87.19 | 87.32 | 86.36 | 87.04 | 75.40 | BSPCA | 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 | LDA | 51.17 | 49.80 | 51.10 | 2.350 | 51.94 | 48.44 | 51.49 | 3.910 | PCA | 83.20 | 81.39 | 82.63 | 67.71 | 85.16 | 83.69 | 84.68 | 71.50 | SPCA | 83.59 | 81.42 | 82.77 | 69.11 | 86.13 | 85.22 | 85.91 | 72.81 | BSPCA | 83.98 | 82.10 | 83.32 | 69.53 | 87.10 | 85.82 | 86.63 | 75.43 |
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The bold values are the maximum of the list.
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