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  • $\begingroup$ High dimension is not a fixed limit of course, in most cases our features are sufficiently expressive that distance works. Of course this might be an important point. Maybe I should have clarified with an example. Say we have a classifier that has an accuracy of 93%, this is acceptable, but now we can either try to improve the classifier or find new features. It all depends on the new possible features and the data, but I was looking for guidelines on this decision. $\endgroup$ Commented Feb 11, 2014 at 14:09
  • $\begingroup$ @Rhand Seems to me that it's a project management level decision. If current solution is acceptable, why tinker with it? It's a waste of time. If it is not acceptable, define more precisely what do you want to improve (speed, accuracy, etc.). $\endgroup$ Commented Feb 11, 2014 at 14:11
  • $\begingroup$ It is not only project management, the question is how to get a maximum accuracy (this is in my question) and what direction is the best to take. You suggest svm and random forest because dimensionality might be too high, that is one possibility I could experiment with to see if accuracy improves and that is the kind of answer I was looking for. $\endgroup$ Commented Feb 11, 2014 at 14:23
  • $\begingroup$ Well, this on the other hand is a very broad question. There are no general rules that classifier X is better than Y. You should just try some number of classifiers and then perform cross-validation for model selection for example. $\endgroup$ Commented Feb 11, 2014 at 15:10