Konstantin S. Sarin

Tomsk state university of control systems and radioelectronics

The use of artificial intelligence systems for critical areas of human life requires trust in the result obtained by the system. An explanation of the obtained solution is important for ensuring trust. Fuzzy systems have the property of explainability due to the presence of a base of production rules in natural language. This work is devoted to the development of a gradient descent algorithm for adjusting the parameters of fuzzy classifiers. Experiments on 38 data sets showed that the use of the developed algorithm for classifiers built by a metaheuristic algorithm statistically significantly increases the accuracy of classification.

fuzzy systems, classification, data analysis, optimization algorithms