Research Article
Multiteam Competitive Optimization Algorithm and Its Application in Bearing Fault Diagnosis
Table 3
The comparison of test samples recognition accuracy for wine.
| | Algorithm | Recognition accuracy 1 | Recognition accuracy 2 | Recognition accuracy 3 |
| | K-MTCO | 97.5% (78/80) | 97.5% (78/80) | 97.5% (78/80) | | (25/25) (31/33) (22/22) | (25/25) (31/33) (22/22) | (25/25) (31/33) (22/22) |
| | S-MTCO | 97.5% (78/80) | 97.5% (78/80) | 96.25% (77/80) | | (25/25) (32/33) (21/22) | (25/25) (32/33) (21/22) | (25/25) (31/33) (21/22) |
| | SVM | 96.25% (77/80) | 97.5% (78/80) | 96.25% (77/80) | | (24/25) (32/33) (21/22) | (25/25) (32/33) (21/22) | (24/25) (32/33) (21/22) |
| | BP | 95% (76/80) | 95% (76/80) | 92.50% (74/80) | | (25/25) (29/33) (22/22) | (25/25) (30/33) (21/22) | (25/25) (29/33) (20/22) |
| | LVQ | 92.50% (74/80) | 92.50% (74/80) | 93.75% (75/80) | | (25/25) (27/33) (22/22) | (25/25) (27/33) (22/22) | (25/25) (28/33) (22/22) |
| | k-NN | k = 1 : 91.25% (73/80) | k = 15 : 96.25% (77/80) | k = 15 : 96.25% (77/80) | | (25/25) (27/33) (21/22) | (25/25) (30/33) (22/22) | (0/25) (33/33) (0/22) |
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