Neural networks don't have a direct equivalent to the 'Prior' argument, but you can achieve similar effects using class weighting or cost-sensitive learning.
One way you con do this in MATLAB is by using crossentropy loss with class weights as described here: https://www.mathworks.com/help/deeplearning/ref/dlarray.crossentropy.html#mw_f6eaac08-f9fa-42ae-8714-3cec2237e548_sep_mw_3097b386-da90-4b07-8553-d1b4c0b68734
classWeights = [0.2, 0.8]; % Example: Adjusting weights for two classes
lossFunction = @(Y, T) crossentropy(Y, T, 'Weights', classWeights);
This ensures that misclassifications in minority classes are penalized more.
Another method is to use oversampling or undersampling. You can oversample the minority class or undersample the majority class to balance the dataset before training.
Can Neural Networks Be Used in fitensemble?
MATLAB's fitensemble does not support neural networks as base learners. Ensemble methods like boosting and bagging typically rely on simpler models (e.g., decision trees, SVMs). However, you can manually create an ensemble of neural networks by training multiple models and averaging their predictions.
