1. A method for classifying and sorting input data in a data stream, comprising: a) sampling the input data with an input control; b) comparing one or more classes of the sampled input data with initially preset data classifications that constitute a baseline for determining mis-classification of data patterns; c) determining a degree of mis-classification of the input data and assigning a probability proportional to the degree of of mis-classification thereto as a criterion for storing the input in a resampling buffer; d) entering data patterns causing mis-classification in a resampling buffer with a probability value proportional to the degree of mis-classification; e) comparing the data patterns to a ground truth source and aligning the data patterns with their associated data pattern labels employing the same mis-classification probability as applied to the resampling buffer to form a set of training data; and f) updating the adaptive classifier to correlate with the training data; and g) repeating steps a)-f) until a sufficient degree of data classification optimization is realized.