Self-Configuring Evolutionary Algorithms Based Design of Hybrid Interpretable Machine Learning Models : доклад, тезисы доклада

Описание

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Workshop “Hybrid methods of modeling and optimization in complex systems” (HMMOCS 2022); Krasnoyarsk; Krasnoyarsk

Год издания: 2022

Идентификатор DOI: 10.15405/epct.23021.38

Ключевые слова: Fuzzy logic classifier, neural network interpretability, self-configuring evolutionary algorithms

Аннотация: 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, iПоказать полностьюt 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.

Ссылки на полный текст

Издание

Журнал: HYBRID METHODS OF MODELING AND OPTIMIZATION IN COMPLEX SYSTEMS

Номера страниц: 313-320

Место издания: London, United Kingdom

Издатель: European Proceedings

Персоны

  • Sherstnev P. A. (Siberian Federal University)

Вхождение в базы данных