16. The method of claim 1 , wherein the number of the at least one removed training case for false nodules exceeds a threshold, the threshold determined with respect to a downscaling factor, x, wherein the number of false nodules remaining in the training set after systematic data scaling is no more than x times the number of true nodules in the training set.
1. A method of improving classification accuracy and reducing false positives in data mining, computer aided-detection, computer-aided diagnosis and artificial intelligence, the method comprising: choosing a training set from a set of training cases using systematic data scaling, the training set including one or more training cases for true nodules and one or more training cases for false nodules, the systematic data scaling removing only one or more training cases for false nodules, which is proximate a classification boundary for true and false nodules, from the training set; and, creating a classifier based on the training set using a classification method, wherein the systematic data scaling method and the classification method produce the classifier thereby reducing false positives and improving classification accuracy.