Hi Arda,
To implement majority voting for your ensemble of deep learning models, follow these steps:
- Train Individual Models:
- Train each of your five deep learning models (ResNet, DarkNet, Xception, AlexNet, SqueezeNet) on your dataset.
- Save the trained models and their predictions.
2. Get Predictions:
- Obtain the prediction results (class labels) from each model for the same test set.
- Ensure the predictions are in a consistent format (e.g., categorical labels).
3. Apply Majority Voting:
- Combine the predictions from the five models into a matrix.
- For each test sample, determine the final class label by taking the mode (most frequent label) of the predictions from the five models.
4. Evaluate Accuracy:
- Compare the final predictions from the ensemble with the true labels of the test set.
- Calculate and report the accuracy of the ensemble model.
Tips for Improved Results
- Use diverse architectures and training data to maximize ensemble benefits.
- Assign higher weights to more accurate models if some outperform others.
- Use cross-validation to ensure robustness and generalization.
- Fine-tune individual models for the best performance before ensembling.
You can refer to this MATLAB answer also: https://in.mathworks.com/matlabcentral/answers/1946388-how-to-apply-ensemble-learning-using-two-trained-deep-learning-models
Refer to this documentation: https://in.mathworks.com/help/stats/framework-for-ensemble-learning.html
