Numerical evaluation of cytologic data. IV. Discrimination and classification.

Abstract

When observed data have to be assigned to one or another category, classification rules are needed. Linear discriminant functions provide easily computed rules; weighing the discriminat function according to the variances in the data sets helps reduce classification errors. Classification on the basis of a probability density involves nonlinear decision boundaries. Simple numerical examples for bivariate feature vectors are worked out to demonstrate these approaches to classification.