hierarchical classification with SVM

Question

I am trying to deal with a problem of classification with SVM, at the beginning I managed to solve the problem at the first level, ie classify my data into 2 classes (class1 and class2). now I want to continue the classification hierarchically ie f I want to separate the second class into two classes. is there a way to do it with Matlab SVM. thank you

Answer 1

You haven't said anything about your features, because after the first classification you would have to define new features for the new classifier.

You can have the features stored in a matrix and use them in the new classifier.

Since I don't know exactly what your problem is, I provided an example without a loop, but you can easily change to a loop if you want.

x1 = 5 * rand(100,1);
y1 = 5 * rand(100,1);
data1 = [x1,y1];
x2 = -5 * rand(100,1);
y2 =  5 * rand(100,1);
data2 = [x2,y2];
x3 = -5 * rand(100,1);
y3 = -5 * rand(100,1);
data3 = [x3,y3];
plot(data1(:,1),data1(:,2),'r.'); hold on
plot(data2(:,1),data2(:,2),'bo');
plot(data3(:,1),data3(:,2),'ms');
data = [data1;data2;data3];

above is my data, representing points in a 2D plane.

Now I will classify them in 2 classes x>0 and x<0.

label = ones(size(data,1),1);
label(1 : size(data1,1)) = -1;
c1 = svmtrain(data,label,'Kernel_Function','linear','showplot',true); 
hold on;
p1 = svmclassify(c1,data);

After the first classifier I choose one class (x<0) and define new a feature.

and I will classify them in 2 classes, y>0 and y<0.

newdata = data(p1 == 1,:);
data1 = newdata(newdata(:,2)>=0,:);
data2 = newdata(newdata(:,2)< 0,:);
data = [data1;data2];
label = ones(size(data,1),1);
label(1 : size(data1,1)) = -1;
c2 = svmtrain(data,label,'Kernel_Function','linear','showplot',true);

I used all the data for training, you can also adjust that to your problem.