Issue in comparing classifiers for pattern recognition I have designed a classifier M which recognizes gestures and classifies it under any category always. A gesture is classified based on the hamming distance between the sample time series y and the training time series x. The result of the classifier are probabilistic values. There are 3 classes/categories with labels A,B,C which classifies hand gestures where there are 100 samples for each class which are to be classified (single feature and data length=100). The data are different time series (x coordinate vs time). The training set is used to assign probabilities indicating which gesture has occured how many times. So,out of 10 training samples if gesture A appeared 6 times then probability that a gesture falls under category A is  


  P(A)=0.6
   similarly 
  P(B)=0.3


and


  P(C)=0.1


Now, I am trying to compare the performance of this classifier with Bayes classifier, K-NN, Principal component analysis (PCA) and Neural Network. 


On what basis,parameter and method should I do it if I consider ROC or cross validate since the features for my classifier are the probabilistic values for the ROC plot hence what shall be the features for k-nn,bayes classification and PCA?
Is there a code for it which will be useful.
What should be the value of k is there are 3 classes of gestures?


Please help. I am in a fix.
There isn't an easy way to do this. In fact a recent paper by Charles Parker explains some of the problems with using ROC curve measurements (there's a friendly overview at my blog - self promotion alert!!). His paper does make some recommendations on the best ways to compare different classifiers, so you will find something useful there.