Im using LIBSVM and MatLab to classify 34x5 data in 3 classes. I applied 10 fold Kfold cross validation method and RBF kernel. The output is this confusion matrix with 0.88 Correct rate (88 % accuracy). This is my confusion matrix
9 0 0
0 3 0
0 4 18
I would like to know what methods inside SVM to consider to improve the accuracy or other classifications method in Machine learning techniques. Any help?
Here is my SVM classification code
load Turn180SVM1; //load data file
libsvm_options = '-s 1 -t 2 -d 3 -r 0 -c 1 -n 0.1 -p 0.1 -m 100 -e 0.000001 -h 1 -b 0 -wi 1 -q';//svm options
C=size(Turn180SVM1,2);
% cross validation
for i = 1:10
indices = crossvalind('Kfold',Turn180SVM1(:,C),10);
cp = classperf(Turn180SVM1(:,C));
for j = 1:10
[X, Z] = find(indices(:,end)==j);%testing
[Y, Z] = find(indices(:,end)~=j);%training
feature_training = Turn180SVM1([Y'],[1:C-1]); feature_testing = Turn180SVM1([X'],[1:C-1]);
class_training = Turn180SVM1([Y'],end); class_testing = Turn180SVM1([X'], end);
% SVM Training
disp('training');
[feature_training,ps] = mapminmax(feature_training',0,1);
feature_training = feature_training';
feature_testing = mapminmax('apply',feature_testing',ps)';
model = svmtrain(class_training,feature_training,libsvm_options);
%
% SVM Prediction
disp('testing');
TestPredict = svmpredict(class_testing,sparse(feature_testing),model);
TestErrap = sum(TestPredict~=class_testing)./length(class_testing)*100;
cp = classperf(cp, TestPredict, X);
disp(((i-1)*10 )+j);
end;
end;
[ConMat,order] = confusionmat(TestPredict,class_testing);
cp.CorrectRate;
cp.CountingMatrix;