Time Series Classification on FordB (test)
91.5AccuracyST
Evaluation results
| Method | Links | |
|---|---|---|
| ST2019.09 | 91.5 | |
| GENDISclassifier=Logistic Regression2019.09 | 89.8 | |
| GENDISclassifier=Ensemble2019.09 | 89.5 | |
| LTS2019.09 | 89 | |
| FS2019.09 | 78.3 | |
| PARALESN (DEEP)Number of recurrent neurons=1024, Architecture=Deep2026.01 | 76.8 | |
| ESN (DEEP)Number of recurrent neurons=1024, Architecture=Deep2026.01 | 76.1 | |
| ResRMNIRecurrent Matrix=Identity2025.08 | 72.6 | |
| RMN2025.08 | 68.5 | |
| PARALESNNumber of recurrent neurons=1024, Architecture=Shallow2026.01 | 68.4 | |
| ESNNumber of recurrent neurons=1024, Architecture=Shallow2026.01 | 62.7 | |
| ResESNIRecurrent Matrix=Identity2025.08 | 61.1 | |
| LeakyESN2025.08 | 60.9 | |
| ResESNRRecurrent Matrix=Random2025.08 | 56.9 | |
| ResESNCRecurrent Matrix=Circular2025.08 | 56.6 | |
| ResRMNCRecurrent Matrix=Circular2025.08 | 55.5 | |
| ResRMNRRecurrent Matrix=Random2025.08 | 55 |