A Regression-Based Linear Classification Procedure

Abstract

A linear regression function is developed for use in a classification procedure. The regression function is chosen to minimize the number of misclassifications and, secondarily, to minimize the sum of absolute residuals. In situations where the categories of classification correspond to ranges of values of a criterion variable in one dimension, this linear classification function is easier to interpret than classical discriminant functions. The procedure is applied to faculty merit review data, resulting in an interpretable regression function and within-sample classifications as good as a four-function discriminant analysis.