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Matlab SVM linear binary classification failure

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Ruben
Ruben on 18 Apr 2016
Commented: Ilya on 28 Apr 2016
I'm trying to implement a simple SVM linear binary classification in Matlab but I got strange results. I have two classes g={-1;1} defined by two predictors varX and varY. In fact, varY is enough to classify the dataset in two distinct classes (about varY=0.38) but I will keep varX as random variable since I will need it to other works.
Using the code bellow (adapted from MAtlab examples) I got a wrong classifier. Linear classifier should be closer to an horizontal line about varY=0.38, as we can perceive by ploting 2D points. It is not displayed the line that should separate two classes! What am I doing wrong?
g(1:14,1)=1;
g(15:26,1)=-1;
m3(:,1)=rand(26,1); %varX
m3(:,2)=[0.4008; 0.3984; 0.4054; 0.4048; 0.4052; 0.4071; 0.4088; 0.4113; 0.4189;
0.4220; 0.4265; 0.4353; 0.4361; 0.4288; 0.3458; 0.3415; 0.3528;
0.3481; 0.3564; 0.3374; 0.3610; 0.3241; 0.3593; 0.3434; 0.3361; 0.3201]; %varY
SVMmodel_testm = fitcsvm(m3,g,'KernelFunction','Linear');
d = 0.005; % Step size of the grid
[x1Grid,x2Grid] = meshgrid(min(m3(:,1)):d:max(m3(:,1)),...
min(m3(:,2)):d:max(m3(:,2)));
xGrid = [x1Grid(:),x2Grid(:)]; % The grid
[~,scores2] = predict(SVMmodel_testm,xGrid); % The scores
figure();
h(1:2)=gscatter(m3(:,1), m3(:,2), g,'br','ox');
hold on
% Support vectors
h(3) = plot(m3(SVMmodel_testm.IsSupportVector,1),m3(SVMmodel_testm.IsSupportVector,2),'ko','MarkerSize',10);
% Decision boundary
contour(x1Grid,x2Grid,reshape(scores2(:,1),size(x1Grid)),[0 0],'k');
xlabel('varX'); ylabel('varY');
set(gca,'Color',[0.5 0.5 0.5]);
hold off

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Accepted Answer

Ilya
Ilya on 18 Apr 2016
This is a consequence of the data being poorly scaled. Do std(m3) and observe that the standard deviations of the two predictors differ by an order of magnitude. Pass Standardize=true to fitcsvm.

  3 Comments

Ruben
Ruben on 18 Apr 2016
Thanks Ilya, I already found that is due to imbalance of predictors. From other forum an user apply the formula bellow to normalize varY m3(:,2) = m3(:,2)./std(m3(:,2));
Ruben
Ruben on 20 Apr 2016
How can we manage SVM model to achieve more false-positives and less false-negatives? Change Bias parameter is not allowed, is there other way to shift hyperplane towards positive or negative class? I know that parameter BoxConstraint can be adapted.
Ilya
Ilya on 28 Apr 2016
Construct a ROC curve using the perfcurve function and find the optimal threshold.

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