You can enhance an Extreme Learning Machine (ELM) by using a Naïve Bayes classifier to compute or adjust its output weight matrix. This involves using the hidden layer outputs of the ELM as features, then estimating class‑conditional probabilities with Naïve Bayes in MATLAB.
1. Prepare hidden layer outputs
- Pass your training data through the ELM’s hidden layer to get a feature matrix (often named "H").
- Please refer to the following documentation for working with matrices and feature engineering:https://www.mathworks.com/help/matlab/matrices-and-arrays.html
2. Train a Naïve Bayes model
- Use the "fitcnb" function to train a Naïve Bayes model by treating the hidden layer outputs as input features and the target labels as response data.
- Please refer to the following documentation for details:https://www.mathworks.com/help/stats/fitcnb.html
3. Estimate output scores
- After training, use the "predict" method of the trained model to compute posterior probabilities or log scores.
- These scores can be used as an alternative to the typical least‑squares solution for ELM output weights.
- Please refer to the following documentation for prediction with trained models:https://www.mathworks.com/help/stats/classificationnaivebayes.predict.html
4. Use estimated scores in your model
- Integrate the computed scores or probabilities as your final output layer weights or activations.
- For general guidance on classification workflow, see:https://www.mathworks.com/help/stats/classification-workflow.html
