MATLAB: How to perform stratified 10 fold cross validation for classification in MATLAB

Computer Vision ToolboxMATLABneural networkstatisticsStatistics and Machine Learning Toolbox

My implementation of usual K-fold cross-validation is pretty much like:
K = 10;
CrossValIndices = crossvalind('Kfold', size(B,2), K);
for i = 1: K
    display(['Cross validation, folds ' num2str(i)])
    IndicesI = CrossValIndices==i;
    TempInd = CrossValIndices;
    TempInd(IndicesI) = [];
    xTraining = B(:, CrossValIndices~=i);
    tTrain = T_new1(:, CrossValIndices~=i);
      xTest = B(:, CrossValIndices ==i);
      tTest = T_new1(:, CrossValIndices ==i);
  end
But To ensure that the training, testing, and validating dataset have similar proportions of classes (e.g., 20 classes).I want use stratified sampling technique.Basic purpose is to avoid class imbalance problem.I know about SMOTE technique but i want to apply this one.

Best Answer

If you have the Statistics and Machine Learning Toolbox, consider the cvpartition function. It can define stratified samples.
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