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

17 vues (au cours des 30 derniers jours)
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.
  3 commentaires

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Réponse acceptée

Tom Lane
Tom Lane le 25 Juil 2017
If you have the Statistics and Machine Learning Toolbox, consider the cvpartition function. It can define stratified samples.
  3 commentaires
Olympia Gallou
Olympia Gallou le 6 Mai 2021
How did you solve your problem?

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Plus de réponses (1)

ashik khan
ashik khan le 18 Nov 2018
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

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