MATLAB provides built-in ensemble learning techniques such as bagging, random space and some boosting algorithms.
You can refer to the following link for more details:
You can implement an ensemble classifier combining Support Vector Machine (SVM) and Artificial Neural Network (ANN) for multiclass data in MATLAB by using a weighted approach. The weight vector can be updated based on classification errors from both models.
The below code segment demonstrates an ensemble learning approach using SVM and ANN with weighted voting using “fisheriris” dataset :
% load dataset
load fisheriris;
data = meas;
labels = grp2idx(species); % Convert categorical labels to numerical
% Split into training and testing dataset
cv = cvpartition(labels, 'HoldOut', 0.3); % 70-30 train-test split is done here
trainData = data(training(cv), :);
trainLabels = labels(training(cv), :);
testData = data(test(cv), :);
testLabels = labels(test(cv), :);
% Train SVM model
svmModel = fitcecoc(trainData, trainLabels);
% Train ANN model
net = patternnet(10); % ANN with 10 hidden neurons
net = train(net, trainData', dummyvar(trainLabels)');
% Initialize weights
w_svm = 0.5;
w_ann = 0.5;
% Predict using SVM
svmPred = predict(svmModel, testData);
% Predict using ANN
annPredProb = net(testData');
[~, annPred] = max(annPredProb, [], 1);
annPred = annPred';
% Compute the classification errors
error_svm = sum(svmPred ~= testLabels) / length(testLabels);
error_ann = sum(annPred ~= testLabels) / length(testLabels);
% Update weights using simple inverse-error approac
w_svm = 1 / (error_svm + 1e-5); % Avoid division by zero error
w_ann = 1 / (error_ann + 1e-5);
w_total = w_svm + w_ann;
% Normalize weights
w_svm = w_svm / w_total;
w_ann = w_ann / w_total;
% Combine predictions using weighted voting
finalPred = mode([repmat(svmPred, 1, round(w_svm*100)) ...
repmat(annPred, 1, round(w_ann*100))], 2);
% Compute final accuracy
accuracy = sum(finalPred == testLabels) / length(testLabels);
fprintf('Ensemble Model Accuracy: %.2f%%\n', accuracy * 100);
You can refer to the below MATLAB documentations for more details:
- fitcecoc: https://www.mathworks.com/help/releases/R2024b/stats/fitcecoc.html
- patternnet: https://www.mathworks.com/help/releases/R2024b/deeplearning/ref/patternnet.html
- train: https://www.mathworks.com/help/releases/R2024b/deeplearning/ref/network.train.html
I hope this helps!
