title: On the neural network classification of medical data and an endeavour to balance non-uniform data sets with artificial data extension.
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.
authors.affiliation: Department of Computer Sciences, 33014 University of Tampere, Finland.
authors.forename: Lassi
authors.initials: L
authors.lastname: Autio
authors.forename: Martti
authors.initials: M
authors.lastname: Juhola
authors.forename: Jorma
authors.initials: J
authors.lastname: Laurikkala
mesh_terms: D000465:Algorithms; D003625:Data Collection / Q000145:classification*; D006801:Humans; D016347:Medical Records Systems, Computerized; D016571:Neural Networks, Computer; D012984:Software