Table 9.
Performance comparison of some prediction models on Cleveland dataset
| Work Ref. | Classifier | Performance metrics | |||||
|---|---|---|---|---|---|---|---|
| ACC | P | R | F1 | S | ERR | ||
| Single classifier based methods | |||||||
| Al-Milli [8] | BPNN | 0.92 | – | – | – | – | – |
| Sonawane and Patil [208] | BPNN | 0.985 | – | 0.98 | – | 0.95 | 0.02 |
| Gavhane et al. [64] | MLP | – | 0.91 | 0.89 | – | – | – |
| Karayılan and Kılıç [113] | BPNN | 0.96 | 0.95 | 0.95 | – | – | – |
| Ismaeel et al. [100] | ELM-NN | 0.87 | – | – | – | – | – |
| Medhekar et al. [136] | NB | 0.90 | – | – | – | – | – |
| Jabbar et al. [101] | NB | 0.86 | – | – | – | – | – |
| Patel et al. [158] | J48 | – | – | – | – | – | 0.15 |
| Dwivedi [56] | LR | 0.85 | 0.85 | 0.89 | – | 0.81 | – |
| Sen [194] | SVM | 0.84 | – | – | – | – | – |
| Shao et al. [200] | MARS-LR | 0.84 | – | – | – | – | – |
| Jayaraman and Sultana [106] | PBAMNN | 1.00 | – | 1.00 | – | 1.00 | – |
| Gokulnath and Shantharajah [69] | GA-SVM | 0.88 | – | – | – | – | – |
| Feshki and Shijani [60] | PSO-FFBP | 0.92 | 0.92 | 0.93 | – | – | – |
| Harimoorthy and Thangavelu [81] | Improved SVM | 0.90 | 0.81 | 0.97 | – | 0.87 | 0.10 |
| Ensemble based methods | |||||||
| Das et al. [48] | Multi-NN | 0.89 | – | – | – | – | – |
| Miao et al. [138] | Boosting | 0.97 | 0.97 | 1.00 | 0.98 | 0.60 | 0.03 |
| Mohan et al. [146] | HRFLM | 0.88 | 0.90 | 0.93 | 0.90 | 0.83 | 0.12 |
| Maji and Arora [130] | DT-NN | 0.78 | – | 0.78 | – | – | 0.23 |
| Amin et al. [14] | Average voting | 0.87 | – | – | – | – | – |