Multiclass classification with SVM a question about the feature vectors I was told I had to direct my machine learning questions to this site. So here it goes.
I'm trying to do Multiclass classification with SVM. I have 7 classes. Now I was wondering if the following is possible. I'm thinking of creating 7 SVMs for 1 vs all approach. Am i allowed to create 1 kind of feature vector per class? 
So e.g.
Class 1 vs rest ==> Use type feature vector 1 (designed for class 1)
Class 2 vs rest ==> Use type feature vector 2 (designed for class 2) 
Class 3 vs rest ==> Use type feature vector 3 (designed for class 3)
And then assign the class-label with the highest confidence (probability), to the datapoint.
Is this cheating ? Or is this allowed ? Is this common practice ?
 A: Just use all the features in the vector. 
Then train your L one-vs-all classifiers, where L is the number of classes. Then upon classifying, choose the class whose classifier returns the highest distance to the hyperplane margin. This is an easy formulation of common practice. 
A stronger approach is to use Error Correcting Code Classifiers (ECOC), which is a very robust method for the [3, 7] class range. You'll need a bit more training time and compute resources ((2^(L-1) - 1) classifiers), but it's very powerful. Here's the best paper on the subject:
Solving Multiclass Learning Problems via
Error-Correcting Output Codes
A: If I understand correctly, from each data instance you'd create L vectors, with i-th vector used in i-vs-rest binary classifier. For testing, you'd do the same for each instance
I think this is legitimate, i.e. no cheating or information leaking that I see.
Having said that, a more general approach would be to create a single representation (at worst by concatenating all L vectors) and use 1-vs-rest. This would allow classifiers to figure out which features are relevant for each class
