I have multiclass unbalanced data (4 class with 15% 25% 45% 15% data in each class). Which method is good for classification of such data- SVM or ANN?
UPDATE- Let me make the question little more general. @Dikran Marsupial said in one answer "choice of classifier depends on the nature of the particular dataset" but what are the factors that one should consider before choosing a classifier. I understand the first chose may not give best answer all the time but it can be a good starting point. So what properties of data I should consider before choosing a classifier??
Best Answer
The no-free lunch theorems suggest there is no classifier that is a priori superior to any other, and the choice of classifier depends on the nature of the particular data set. I wouldn't cmomit myself to a choice of classifier and would instead evaluate several methods.
The classes are only mildly imbalanced, so I suspect that shouldn't be a key factor in the decision of which classifier to use.
A more important question would be whether you wanted a simple discrete classification, or whether you wanted estimates of the probabilities of class membership, for examples because you have unknown or variable mis-classification costs, or relative class frequencies, or if it would be beneficial to have a "reject" option. In that case the SVM is not a good choice as it is designed for discrete classification, and rather than post-processing the output to get probabilities it is better to use a method that was designed to provide a probabilistic output in the first place, such as kernel logistic regression.