Abstract
We studied the efficiency of multilayer perceptron networks to classify eight different medical data sets with typical problems connected to their strongly non-uniform distributions between output classes and relatively small sizes of training sets. We studied especially the possibility mentioned in the literature of balancing a class distribution by artificially extending small classes of a data set. The results obtained supported our hypothesis that principally this does somewhat improve the classification accuracy of small classes, but is also inclined to impair the classification accuracy of majority classes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
