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

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.

[Leyre Azcárate Bescós]

What is B?

[Leyre Azcárate Bescós]

And T_new1?

[uma]

what is T_new1

Answer 1

[Tom Lane]

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

[Machine Learning Enthusiast]

yes,i have Statistics and Machine Learning Toolbox.But,how can i implement cvpartition in below code

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

[Olympia Gallou]

What is the B and T_new?

[Olympia Gallou]

How did you solve your problem?

Answer 2

[ashik khan]

What are the value of B and T_new1 ??

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