Clustering algorithms application to forming a representative sample in the training of a multilayer perceptron

Abstract

In this paper, we have considered the problem of effectively forming the representative sample for training a neural network of the multilayer perceptron (MLP) type. An approach based on the use of clustering that allowed to increase the entropy of the training set was put forward. Various clustering algorithms were examined in order to form the representative sample. The algorithm-based clustering of factor spaces of various dimensions was carried out, and a representative sample was formed. To verify our approach we synthesized the MLP neural network and trained it. The training technique was performed with the sets formed both with and without clustering. A comparative analysis of the effectiveness of clustering algorithms was carried out in relation to the problem of representative sample formation.