Self-Configuring Evolutionary Algorithms Based Design of Hybrid Interpretable Machine Learning Models

Abstract

The paper describes an approach in which the decision-making process of an artificial neural network is interpreted by a fuzzy logic system. A neural network and a fuzzy system are automatically designed with the use of the self-configuring evolutionary algorithms. Experiments are carried out on classification tasks. As a result, it is shown that the building of a fuzzy system on the inputs and outputs of a neural network allows one to build an interpreted rule base of a smaller size, as if this rule base were built on the data of the original problem. In addition, the accuracy of such a system is comparable to the accuracy of a fuzzy system trained on the original task. As a result, the researcher has a neural network with high accuracy of solving the problem, as well as a fuzzy system explaining the neural network’s decision-making process. The article presents some constructed rule bases and neural networks for interpretation of which they were built.

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About this article

Publication Date

27 February 2023

eBook ISBN

978-1-80296-960-3

Publisher

European Publisher

Volume

1

Print ISBN (optional)

-

Edition Number

1st Edition

Pages

1-403

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Cite this article as:

Sherstnev, P. A. (2023). Self-Configuring Evolutionary Algorithms Based Design of Hybrid Interpretable Machine Learning Models. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 313-320). European Publisher. https://doi.org/10.15405/epct.23021.38