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 category(-ies)
• use a boosting algorithm like Adaboost http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
• use cost-sensitive classification algorithm

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