1. A computer implemented method for multi-class, cost-sensitive learning for a process selected from the group consisting of network intrusion detection, fraud detection, targeted marketing, and credit risk rating, wherein said computer implemented method is based on an example weighting scheme applied to a chosen data set comprising the steps of: a) obtaining a data set from a data storage module, said data set comprising an original data set enhanced with additional data points corresponding in number to a number of labels for a single instance; b) iteratively applying weighted sampling from said data set, using a dynamically changing weighting scheme involving both positive and negative weights obtained from an example weights storage module; c) calling a classification learning algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively, and obtains a hypothesis representing a classifier; d) outputting all representations obtained through the iterations and representing an average over them, each of which is an arbitrary representation of the classifier, said average being used for one of network intrusion detection, fraud detection, targeted marketing, and credit risk rating.