Rule generation from neural networks

1994/01/01 by LiMin Fu · 368 citations
Computer Science · Engineering · #Artificial intelligence #Artificial neural network #Computer science #Disadvantage #Fault Detection and Control Systems #Fuzzy Logic and Control Systems #Interpretation (philosophy) #Machine learning #Neural Networks and Applications #Programming language #Soundness

paper · doi:10.1109/21.299696

published in IEEE Transactions on Systems Man and Cybernetics 24(8), 1114-1124 (Institute of Electrical and Electronics Engineers)

openalex publication_date 1994/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

The neural network approach has proven useful for the development of artificial intelligence systems. However, a disadvantage with this approach is that the knowledge embedded in the neural network is opaque. In this paper, we show how to interpret neural network knowledge in symbolic form. We lay dawn required definitions for this treatment, formulate the interpretation algorithm, and formally verify its soundness. The main result is a formalized relationship between a neural network and a rule-based system. In addition, it has been demonstrated that the neural network generates rules of better performance than the decision tree approach in noisy conditions.>

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• Principles and Practice of Explainable Machine Learning. [europepmc]
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