The sampling method preserving interclass boundaries

Abstract

The sample dimensionality reduction problem for classification is addressed. The sampling method with the preservation of the most significant instances near the interclass boundaries is proposed. It calculates the interclass distances in the sample used to build hyperspheres, removes redundant instances inside the hyperspheres, and creates a subsample from the set of hypersphere centers. The experiments to study the proposed method properties are conducted. They allow recommending the proposed method for use in practice as a significant to reduce the complexity and ensure acceptable accuracy of classification models.