Take the 2-minute tour ×
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It's 100% free, no registration required.

What would be the best approach to choosing the multi-class strategy (MSVM, OVA, ECOC) for classifying a large number of classes with limited examples of each class?

Another two factors that define the solution, in my case:

  • The probability of prediction is an important factor that decides the next steps in my solution.
  • The size (i.e. size of the classifier object on disk which can later be loaded in memory and used for prediction), of the fitted classifier (since I need the solution to be portable without providing it as a service). I do not need to analyze the classifier(s) any more than getting the category with reasonable probability.
    UPDATE: The problem is basically that of text classification.
share|improve this question
    
Would you elaborate more on what you mean by the "size" of the classifier i.e. disk storage or complexity or what ? –  soufanom Feb 26 '13 at 4:33
    
Size as in disk storage...as of now I am trying to get the answers without limiting the features and accuracy. –  Ark Mar 2 '13 at 6:23

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Browse other questions tagged or ask your own question.