Re: Classifier Processor Usage with more than 2 classes.
Posted: Fri May 06, 2011 4:53 pm
Ok, that clarifies a lot I thought the algorithms were ready for multiclass, but somehow the "add settings" option was not available in the processing box. For the one versus all scheme there's always the possibility to create different binary classifiers and use the voting box. Nevertheless, I just wondered if there was something wrong with my update.
Some inner thoughts:
From a design point of view it's indeed very difficult as there does not seem to be a straightforward way to encode the output of three "machines" (in case of one versus all in the three class case) into one real output stream.
At this point however, I don't see the compability issue. If you output the class label l1 and all the probabilities of each class (p1, p2, p3...), then in case of a two-class classifier the second output p1 can directly be connected to the input of the same box just like before, the third one (p2) simply remains unused? Or do you mean that you will have one output for the label and one output that sends a matrix or rather vector of probabilities? Why then not simply use different output connectors per class? The number of classes will probably never be that high.
Regression algorithms do not have this problem, only one real output
Would it not be possible to rank all probability vectors, starting with an object closest to class 0 according to its probabilities and ending with the one closest to class K, then subdividing the interval [0 1] in parts, associating each probability vector with a single value of that interval and fit some regression function between the probability vectors and the computed values in the interval?
Best regards,
Dieter Devlaminck
Some inner thoughts:
From a design point of view it's indeed very difficult as there does not seem to be a straightforward way to encode the output of three "machines" (in case of one versus all in the three class case) into one real output stream.
At this point however, I don't see the compability issue. If you output the class label l1 and all the probabilities of each class (p1, p2, p3...), then in case of a two-class classifier the second output p1 can directly be connected to the input of the same box just like before, the third one (p2) simply remains unused? Or do you mean that you will have one output for the label and one output that sends a matrix or rather vector of probabilities? Why then not simply use different output connectors per class? The number of classes will probably never be that high.
Regression algorithms do not have this problem, only one real output
Would it not be possible to rank all probability vectors, starting with an object closest to class 0 according to its probabilities and ending with the one closest to class K, then subdividing the interval [0 1] in parts, associating each probability vector with a single value of that interval and fit some regression function between the probability vectors and the computed values in the interval?
Best regards,
Dieter Devlaminck