Suggestions to improve my normalized accuracy with libsvm

Question

I'm with a problem when I try to classify my data using libsvm. My training and test data are highly unbalanced. When I do the grid search for the svm parameters and train my data with weights for the classes, the testing gives the accuracy of 96.8113%. But because the testing data is unbalanced, all the correct predicted values are from the negative class, which is larger than the positive class.

I tried a lot of things, from changing the weights until changing the gamma and cost values, but my normalized accuracy (which takes into account the positive classes and negative classes) is lower in each try. Training 50% of positives and 50% of negatives with the default grid.py parameters i have a very low accuracy (18.4234%).

I want to know if the problem is in my description (how to build the feature vectors), in the unbalancing (should i use balanced data in another way?) or should i change my classifier?

Answer 1

Better data always helps.

I think that imbalance is part of the problem. But a more significant part of the problem is how you're evaluating your classifier. Evaluating accuracy given the distribution of positives and negatives in your data is pretty much useless. So is training on 50% and 50% and testing on data that is distributed 99% vs 1%.

There are problems in real life that are like the one your studying (that have a great imbalance in positives to negatives). Let me give you two examples:

• Information retrieval: given all documents in a huge collection return the subset that are relevant to search term q.

• Face detection: this large image mark all locations where there are human faces.

Many approaches to these type of systems are classifier-based. To evaluate two classifiers two tools are commonly used: ROC curves , Precision Recall curves and the F-score . These tools give a more principled approach to evaluate when one classifier is working better than the another.