1. A method performed by a computer system for classifying data with a classification system when training data that is used to train the classification system potentially contains errors, comprising: dividing a full set of the training data into a positive subset and a negative subset, the positive subset containing all training data with a positive classification value and the negative subset containing all training data with a negative classification value; creating at least two randomly constructed subsets of the full set of training data wherein each randomly created subset contains data selected with equal probability from both the positive subset and the negative subset further wherein a count of data in each randomly created subset is less than a count of the full set of training data; and creating at least two classifiers based on each of the randomly created subsets of the training data.