Neural Network Classifier Adaptation to Unstable Input Data
https://doi.org/10.35596/1729-7648-2025-23-5-99-104
Abstract
Today, classification problems are typically solved using neural networks. When choosing a network architecture, particular attention is paid to compression layers, while the multilayer perceptron architecture is chosen intuitively, although the quality of the solution largely depends on the type of separating surface. This article proposes an algorithm for testing the effectiveness of a multilayer perceptron architecture based on an analysis of input data properties. The algorithm is based on a special numerical metric for assessing the effectiveness of a neural network architecture, the skew coefficient, developed by the authors. The coefficient is calculated based on the confusion matrix, which does not require a large amount of calculations. Experiments have shown that choosing the right classifier architecture can improve solution quality by 50 %.
About the Authors
V. V. Matskevich
Belarusian State University
Belarus
Belarus
Vadim V. Matskevich, Cand. Sci. (Tech.), Associate Professor at Information Management Systems Department,
4, Nezavisimosti Ave., Minsk, 220030.
Тел.: +375 29 125-49-07.
Jiran Guo
Belarusian State University
Belarus
Belarus
Jiran Guo, Postgraduate at Information Management Systems Department,
Minsk.
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Review
For citations:
Matskevich V.V., Guo J. Neural Network Classifier Adaptation to Unstable Input Data. Doklady BGUIR. 2025;23(5):99-104. (In Russ.) https://doi.org/10.35596/1729-7648-2025-23-5-99-104
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