1. A method comprising: receiving a training dataset comprising a plurality of tuples and a plurality of attributes for each of the tuples; deriving a plurality of granules from the training dataset, each granule comprising a plurality of sample tuples and a plurality of sample attributes, wherein for each of the plurality of granules: the plurality of sample tuples is randomly selected from among the plurality of tuples with replacement; and the plurality of sample attributes is randomly selected from among the plurality of attributes without replacement; processing the granules using a support vector machine process to identify a hyperplane classifier associated with each of the granules; predicting a classification of a new tuple using each of the hyperplane classifiers to produce a plurality of predictions; aggregating the predictions to derive a decision on a final classification of the new tuple; validating a first hyperplane classifier associated with a granule by classifying a plurality of tuples from the training dataset which were not included in the granule; generating a hyperplane classifier effectiveness level based upon the validation of the first hyperplane classifier against tuples from the training dataset which were not included in the granule; determining whether the hyperplane classifier effectiveness level exceeds a threshold effectiveness level; and in response to determining that the hyperplane classifier effectiveness level does not exceed the threshold effectiveness level: removing the first hyperplane classifier; and requesting a second plurality of sample attributes, each sample attribute in the second plurality of sample attributes different from each sample attribute in the plurality of sample attributes, for use in identifying new hyperplane classifiers.