Sounds cool. I think the most crucial part is really the data (or feature) set. The choice of classifiers makes a difference too, but having good data (salient, invariant, discriminatory) makes the largest difference.

One think about naive Bayes and kNN: Although the performance may be similar, I would consider Naive beneficial if the data set is large. kNN requires to re-evaluate the data. Naive Bayes makes the classification computationally cheaper and with "on-line" learning, you can still improve or adjust it over time