It really depends on your data but there are at least four things you could try:
- Upsample the training set by copying the examples in each category
- Downsample the training set by deleting some examples from the dominating categories
- Use a boosting algorithm like scikit-learn's Adaboost
- Use cost-sensitive classification algorithm
