Patent ID: 8275721

Claim:
A computer-implemented method for multi-class, cost-sensitive learning based on iterative example weighting schemes applied to a chosen data set, comprising: a) obtaining an expanded data set, which is defined by enhancing each example in an original data set with as many data points as there are possible labels for any single instance; b) repeatedly selecting one or more sub-samples from the expanded data set to construct an objective to be minimized using weighted sampling according to a predetermined example weighting scheme, wherein each non-optimally labeled example is given a weight which equals a half times an original misclassification cost for said labeled example times p−1 norm of average prediction of current hypotheses, and each optimally labeled example is given a weight which equals sum of weights for all the non-optimally labeled examples for the same instance; c) executing a component classification learning algorithm on the sub-sample obtained in step b) and obtaining a hypothesis representing a classifier; and d) outputting all classifier representations obtained in iterations of steps b) and c), each of said classifier representations being a representation of classifier.