Your tree sounds like it is failing to find any attributes that provide a meaningful split to separate the labels. Did you try any of the other criterion (information gain, gini index) and also the confidence parameter?
You might want to see whether your attributes have any predictive relationship with your label. Try a simpler approach like some of the "weight" operators first, like weight by information gain or weight by gini index. That will show you whether you have attributes that can separate your classes at all. You can also run a simple Naive Bayes model and look at the output, which will show the class distributions. If they are not distinct, then your decision tree is not going to find anything to use for a split.