Hemisphere Classification on Necker cube
100ROC AUCRandom Forest
Evaluation results
| Method | Links | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Random ForestFrequency Band=Delta2025.09 | 100 | 98 | 95 | 100 | 98 | 96 | 100 | 96 | |
| Random ForestFrequency Band=Theta2025.09 | 100 | 98 | 95 | 100 | 98 | 96 | 100 | 96 | |
| Random ForestFrequency Band=Alpha2025.09 | 100 | 94 | 88 | 100 | 93 | 88 | 100 | 87 | |
| Random ForestFrequency Band=Beta2025.09 | 100 | 98 | 100 | 95 | 98 | 100 | 95 | 96 | |
| Random ForestFrequency Band=Gamma2025.09 | 100 | 94 | 88 | 100 | 93 | 88 | 100 | 87 | |
| SVM (Linear)Frequency Band=Delta2025.09 | 100 | 98 | 100 | 95 | 98 | 100 | 95 | 96 | |
| SVM (Linear)Frequency Band=Theta2025.09 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
| SVM (Linear)Frequency Band=Alpha2025.09 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
| SVM (Linear)Frequency Band=Beta2025.09 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
| SVM (Linear)Frequency Band=Gamma2025.09 | 100 | 98 | 95 | 100 | 98 | 96 | 100 | 96 | |
| SVM (RBF)Frequency Band=Delta2025.09 | 99 | 94 | 95 | 90 | 93 | 96 | 90 | 87 | |
| SVM (RBF)Frequency Band=Theta2025.09 | 99 | 96 | 95 | 95 | 95 | 96 | 95 | 91 | |
| SVM (RBF)Frequency Band=Alpha2025.09 | 99 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
| SVM (RBF)Frequency Band=Beta2025.09 | 99 | 98 | 95 | 100 | 98 | 96 | 100 | 96 | |
| SVM (RBF)Frequency Band=Gamma2025.09 | 99 | 96 | 91 | 100 | 95 | 92 | 100 | 91 | |
| Small machine learning modelFrequency=β2025.09 | 98.3 | — | — | — | — | — | — | — | |
| Big machine learning modelFrequency=β, Optimizer=Adadelta2025.09 | 98.15 | — | — | — | — | — | — | — | |
| Deep Neural Network (RMSprop)Frequency Band=Delta2025.09 | 98 | 91 | 95 | 86 | 90 | 96 | 86 | 83 | |
| Deep Neural Network (RMSprop)Frequency Band=Theta2025.09 | 98 | 98 | 95 | 100 | 98 | 96 | 100 | 96 | |
| Deep Neural Network (RMSprop)Frequency Band=Alpha2025.09 | 98 | 98 | 100 | 95 | 98 | 100 | 95 | 96 | |
| Deep Neural Network (RMSprop)Frequency Band=Beta2025.09 | 98 | 100 | 100 | 100 | 100 | 100 | 100 | 100 | |
| Deep Neural Network (RMSprop)Frequency Band=Gamma2025.09 | 98 | 96 | 95 | 95 | 95 | 96 | 95 | 91 | |
| Small machine learning modelFrequency=δ, Optimizer=Adam2025.09 | 97.94 | — | — | — | — | — | — | — | |
| Big machine learning modelFrequency=γ, Optimizer=Adadelta2025.09 | 97.21 | — | — | — | — | — | — | — | |
| Small machine learning modelFrequency=θ, Optimizer=RMSprop2025.09 | 96.92 | — | — | — | — | — | — | — | |
| Small machine learning modelFrequency=γ, Optimizer=NAdam2025.09 | 96.44 | — | — | — | — | — | — | — | |
| Small machine learning modelFrequency=α, Optimizer=RMSprop2025.09 | 95.68 | — | — | — | — | — | — | — | |
| Big machine learning modelFrequency=α, Optimizer=Adadelta2025.09 | 95.1 | — | — | — | — | — | — | — | |
| Big machine learning modelFrequency=δ, Optimizer=Adadelta2025.09 | 94.74 | — | — | — | — | — | — | — | |
| Convolutional Neural Network (CNN)Frequency=δ2025.09 | 94.1 | — | — | — | — | — | — | — | |
| Big machine learning modelFrequency=θ, Optimizer=Adadelta2025.09 | 93.01 | — | — | — | — | — | — | — | |
| Convolutional Neural Network (CNN)Frequency=β, Optimizer=NAdam2025.09 | 91.98 | — | — | — | — | — | — | — | |
| Convolutional Neural Network (CNN)Frequency=α2025.09 | 89.65 | — | — | — | — | — | — | — | |
| Convolutional Neural Network (CNN)Frequency=θ, Optimizer=SGD2025.09 | 88.37 | — | — | — | — | — | — | — | |
| Convolutional Neural Network (CNN)Frequency=γ, Optimizer=Adamax2025.09 | 86.95 | — | — | — | — | — | — | — |