Example of 10-fold SVM classification in MATLAB

Question

I need a somehow descriptive example showing how to do a 10-fold SVM classification on a two class set of data. there is just one example in the MATLAB documentation but it is not with 10-fold. Can someone help me?

Answer 1

Here's a complete example, using the following functions from the Bioinformatics Toolbox: SVMTRAIN, SVMCLASSIFY, CLASSPERF, CROSSVALIND.

load fisheriris                              %# load iris dataset groups = ismember(species,'setosa');         %# create a two-class problem  %# number of cross-validation folds: %# If you have 50 samples, divide them into 10 groups of 5 samples each, %# then train with 9 groups (45 samples) and test with 1 group (5 samples). %# This is repeated ten times, with each group used exactly once as a test set. %# Finally the 10 results from the folds are averaged to produce a single  %# performance estimation. k=10;  cvFolds = crossvalind('Kfold', groups, k);   %# get indices of 10-fold CV cp = classperf(groups);                      %# init performance tracker  for i = 1:k                                  %# for each fold     testIdx = (cvFolds == i);                %# get indices of test instances     trainIdx = ~testIdx;                     %# get indices training instances      %# train an SVM model over training instances     svmModel = svmtrain(meas(trainIdx,:), groups(trainIdx), ...                  'Autoscale',true, 'Showplot',false, 'Method','QP', ...                  'BoxConstraint',2e-1, 'Kernel_Function','rbf', 'RBF_Sigma',1);      %# test using test instances     pred = svmclassify(svmModel, meas(testIdx,:), 'Showplot',false);      %# evaluate and update performance object     cp = classperf(cp, pred, testIdx); end  %# get accuracy cp.CorrectRate  %# get confusion matrix %# columns:actual, rows:predicted, last-row: unclassified instances cp.CountingMatrix 

with the output:

ans =       0.99333 ans =    100     1      0    49      0     0 

we obtained 99.33% accuracy with only one 'setosa' instance mis-classified as 'non-setosa'

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UPDATE: SVM functions have moved to Statistics toolbox in R2013a