how to ensemble 5 different deep learning model with majority voting?

here i saw ensemble deep learning models and they get better results in ensemble learning

https://blogs.mathworks.com/deep-learning/2019/06/03/ensemble-learning/

now i try 5 different deep learning system (for example: resnet, darknet, xception, alexnet, sequeezneet) for my image dataset (for example tumor detection healthy show "0" tumour shows"1" in dataset. i get accuracyresults for these networks but i coudn't find prediction matrix. so my aim is applying majority voting on these 5 deep learning networks and improve my results?

do you have any suggestions?

Answer 1

[Shubham]

Hi Arda,

To implement majority voting for your ensemble of deep learning models, follow these steps:

1. 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

1. Use diverse architectures and training data to maximize ensemble benefits.
2. Assign higher weights to more accurate models if some outperform others.
3. Use cross-validation to ensure robustness and generalization.
4. 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