How to train the classifier (using features extracted from images)?

I would like to train the Random forest classifier( which has 2 classes- pathology class(Tp) and non pathology class(Tn)). I have separate images to train & test the classifier. For feature extraction I should use HOG, GLCM, GLRLM. How do I train & test the classifier Using these extracted features?? I don't have any .mat file to train the classifier, I see most of the code uses mat file to train the classifier. So I don't have any idea to proceed this. Please help me with this.

Answer 1

[Image Analyst]

Use the fitctree fucntion to create a classification tree based on the training data:

tModel = fitctree(xTrain, yTrain);

See what you can do with tModel by looking at its methods:

methods(tModel)

The resulting tree can be visualized with the view() function:

view(tModel, 'mode', 'graph');

New observations can be classified using the predict() function:

yPredicted = predict(tModel, newX);

The TreeBagger() function uses bootstrap aggregation ("bagging") to create an ensemble of classification trees.

tModel = TreeBagger(50, xTrain, yTrain); % Create new model based on 50 trees.

This is a more robust model.

[revathi t]

Sir Could you explain me, what is xTrain & yTrain? (I have written the extracted features in train.xls)

[Image Analyst]

Those would be the values of HOG, GLCM, and GLRLM that you measured.