Open AccessRule extraction from the Artificial Neural Network
Author(s) -
Daniil Marshakov
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1029/1/012127
Subject(s) - artificial neural network , computer science , artificial intelligence , field (mathematics) , structuring , machine learning , set (abstract data type) , transformation (genetics) , dimension (graph theory) , neuro fuzzy , data mining , fuzzy logic , pattern recognition (psychology) , fuzzy control system , mathematics , biochemistry , chemistry , finance , pure mathematics , economics , gene , programming language
Applying machine learning techniques to various critical decision-making domains implies the need to explain why a machine learning algorithm makes certain conclusions. Artificial neural networks (ANNs) have achieved high classification accuracy for many classification problems, but their results can’t be interpreted, which is why ANNs are often considered as «black box». To interpret the results of neural network classification, the paper presents a method for extracting rules from ANN. The proposed method is based on the structuring of information flows processed in the ANN information field through transforming complex multidimensional data into a simpler structure of a lower dimension, followed by a trivial transformation of the results into a set of fuzzy rules of a certain type. The implementation of the proposed method provides for the construction of the hybrid ANN configuration based on self-organizing and multilayer ANNs. The results of an experimental research of the proposed method are presented on the example of solving the well-known problem of multiparameter classification. The results obtained confirm the adequacy of the proposed method, which can be used both in independent neural network pattern recognition systems and within decision support systems.