10 fold cross-validation in one-against-all SVM (using LibSVM)

Question

I want to do a 10-fold cross-validation in my one-against-all support vector machine classification in MATLAB.

I tried to somehow mix these two rela

Answer 1

It may be confusing you that one of the two questions is not about LIBSVM. You should try to adjust this answer and ignore the other.

You should select the folds, and do the rest exactly as the linked question. Assume the data has been loaded into data and the labels into labels:

n = size(data,1);
ns = floor(n/10);
for fold=1:10,
    if fold==1,
        testindices= ((fold-1)*ns+1):fold*ns;
        trainindices = fold*ns+1:n;
    else
        if fold==10,
            testindices= ((fold-1)*ns+1):n;
            trainindices = 1:(fold-1)*ns;
        else
            testindices= ((fold-1)*ns+1):fold*ns;
            trainindices = [1:(fold-1)*ns,fold*ns+1:n];
         end
    end
    % use testindices only for testing and train indices only for testing
    trainLabel = label(trainindices);
    trainData = data(trainindices,:);
    testLabel = label(testindices);
    testData = data(testindices,:)
    %# train one-against-all models
    model = cell(numLabels,1);
    for k=1:numLabels
        model{k} = svmtrain(double(trainLabel==k), trainData, '-c 1 -g 0.2 -b 1');
    end

    %# get probability estimates of test instances using each model
    prob = zeros(size(testData,1),numLabels);
    for k=1:numLabels
        [~,~,p] = svmpredict(double(testLabel==k), testData, model{k}, '-b 1');
        prob(:,k) = p(:,model{k}.Label==1);    %# probability of class==k
    end

    %# predict the class with the highest probability
    [~,pred] = max(prob,[],2);
    acc = sum(pred == testLabel) ./ numel(testLabel)    %# accuracy
    C = confusionmat(testLabel, pred)                   %# confusion matrix
end