Does classification of a balanced data-set lead to any problem?
Actually, I guess it highly depends on the real data-set and its distribution. I guess the paper has referred to that is that on occasions that the distribution of each class varies, your model won't work well because of changing the distribution of each class. In cases like a disease prediction where the number of each class varies for different places, a model that is trained in the U.S won't work in African countries at all. The reason is that the distribution of classes has been changed. So in such cases that usually the negative and positive classes are not balanced in practice, balancing them will cause the problem of distribution changes. On these occasions, people usually use the real data-set which is not balanced and use F1 score for evaluation.