Question

I am learning about SVM and ROC. As I know, people can usually use a ROC(receiver operating characteristic) curve to show classification ability of a SVM (Support Vector Machine). I am wondering if I can use the same concept to compare two subsets of features.

Assume I have two subsets of features, subset A and subset B. They are chosen from the same train data by 2 different features extraction methods, A and B. If I use these two subsets of features to train the same SVM by using the LIBSVM svmtrain() function and plot the ROC curves for both of them, can I compare their classification ability by their AUC values ? So if I have a higher AUC value for subsetA than subsetB, can I conclude that method A is better than method B ? Does it make any sense ?

Thank you very much,

Answer 1

Yes, you are on the right track. However, you need to keep a few things in mind.

1. Often using the two features A and B with the appropriate scaling/normalization can give better performance than the features individually. So you might also consider the possibility of using both the features A and B together.
2. When training SVMs using features A and B, you should optimize for them separately, i.e. compare the best performance obtained using feature A with the best obtained using feature B. Often the features A and B might give their best performance with different kernels and parameter settings.
3. There are other metrics apart from AUC, such as F1-score, Mean Average Precision(MAP) that can be computed once you have evaluated the test data and depending on the application that you have in mind, they might be more suitable.