I'm facing a text classification problem, and I need to classify examples to 34 groups.
The problem is, the size of training data of 34 groups are not balanced. For some groups I have 2000+ examples, while for some I only have 100+ examples.
For some small groups, the classification accuracy is quite high. I guess those groups may have specific key words to recognize and classify. While for some, the accuracy is low, and the prediction always goes to large groups.
I want to know how to deal with the "low frequency example problem". Would simply copy and duplicate the small group data work? Or I need to choose the training data and expand and balance the data size? Any suggestions?
Regularization can sometimes help imbalanced class problems by reducing the effect of spurious correlation, but that depends on your data. One solution is to simply over-sample the smaller classes, or increase the weights of the data points in the smaller classes to force the classifier to pay more attention to it.
You can find more advanced techniques by searching for "class imbalance" problems. Though not as many of them have been applied / created for text classification problems, as it is very common to have huge amounts of data when working with text problems. So I'm not sure how many work well in such high dimensional space.
来源：https://stackoverflow.com/questions/17618532/how-to-deal-with-low-frequency-examples-in-classification