IMPROVEMENT OF ACCURACY OF PARAMETRIC CLASSIFICATION IN THE SPACE OF N×2 FACTORS-ATTRIBUTES ON THE BASIS OF PRELIMINARY OBTAINED LINEAR DISCRIMINANT FUNCTION

Abstract

A procedure for classifying objects in the space of N×2 factors-attributes that are incorrectly classified as a result of constructing a linear discriminant function is proposed. The classification accuracy is defined as the proportion of correctly classified objects that are incorrectly classified at the first stage of constructing a linear discriminant function. It is shown that, for improperly classified objects, the transition from use as the factors-attributes of their initial values to the use of the centers of gravity (COGs) of local clusters provides the possibility of improving the classification accuracy by 14%. The procedure for constructing local clusters and the principle of forming a classifying rule are proposed, the latter being based on converting the equation of the dividing line to the normal form and determining the sign of the deviation magnitude of the COGs of local clusters from the dividing line