How to improve the accuracy of the decision tree in matlab

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I have a set of data which I classify them in matlab using decision tree. I divide the set into two parts; one training data(85%) and the other test data(15%). The problem is that the accuracy is around %90 and I do not know how I can improve it. I would appreciate if you have any idea about it.


I guess the more important question here is what's a good accuracy for the given domain: if you're classifying spam then 90% might be a bit low, but if you're predicting stock prices then 90% is really high!

If you're doing this on a known domain set and there are previous examples of classification accuracy which is higher than yours, then you can try several things: