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.
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. 13. An article of manufacture which is an imaging device or a false positive reduction device, wherein the device is a computer that is programmed to analyze image data by implementing the method of claim 1 .