The invention relates to improved methods and computer-based systems and software products useful for deriving and optimizing linear classifiers based on an adjusted sparse linear programming methodology (A-SPLP). This methodology is based on minimizing an objective function, wherein the objective function includes a loss term representing the performance of the objective function on a training dataset comprising at least two separate, adjustable weighting constants associated with classification errors for data points in-class and not-in-class, respectively.