kNN: how to improve Spam classification?
You could consider querying the k-nearest neighbours and weight them following some schema like this one. There are quite a few in the literature. The general idea is to give more relevance to those neighbours lying closer to your sample. It has the effect of regularizing your classifiers (smooth out your decision surfaces).

You evaluated your algorithm using 10-fold CV. What is the dispersion of your measurements?. If you see wide dispersion, you could use bagging. It is very easy to implement. It is specially meaningful when overfitting is strong.