1. A computer-implemented method of selecting a training data set from a plurality of sets of stored real-world event data for training at least one computer-implemented classifier, the training data set having a distribution approximating an independent identical distribution representative of a real-world distribution, the method comprising: selecting at least a first set of event data as the training data set; repeating, until a criteria for selecting the training data set is satisfied, the steps of: randomly selecting a first set of event data and a second set of event data; generating for the first and second sets of event data respective proximity values with respect to the training data set; selectively adding one of the first or second sets of event data to add to the training data set based on the respective proximity values; and storing the training data set for use in training the at least one classifier.