Table 7.
Comparison of our fuzzy classifiers with other fuzzy techniques.
| Sr. No. | Reference | Methods | Classification Accuracy |
|---|---|---|---|
| 1 | Siva et al. [23] | Fuzzy rules and grey wolf optimization (GWO) algorithm | 81% |
| 2 | Cheruku et al. [24] | Rough Set Theory (RST) and the Bat Optimization Algorithm | 85.33% |
| 3 | Singh et al. [25] | Fuzzy rule miner (ANT FDCSM). | 87.7% |
| 4 | Lukmanto et al. [26] | Fuzzy support vector machine | 89.02% |
| 5 | Thungrut et al. [28] | Fuzzy genetic algorithm | 87.40% |
| 6 | Mujawar et al. [30] | Fuzzy expert system | 84% |
| 7 | Chen et al. [31] | Neuro-fuzzy | 75.67% |
| 8 | Mansourypoor et al. [10] | Fuzzy rule-based system | 82.5% and 96.5% |
| 9 | Vaishali et al. [32] | Multiple objective evolutionary fuzzy classifier | 83.0435% |
| 10 | Geman et al. [33] | Adaptive Neuro-Fuzzy Inference System | For training data, 85.35% and testing data, is 84.27% |
| 11 | Bhuvaneswari et al. [34] | Temporal fuzzy ant miner tree | 83.7% |
| 12 | Deshmukh et al. [35] | Fuzzy CNN | 95% |
| 13 | Fuzzy classifier 1 | Fuzzy | 96.47% |
| 14 | Fuzzy classifier 2 | Fuzzy | 95.38% |