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Neural Computing and Applications

Erschienen in:

01.06.2008 | Original Article

Designing a decompositional rule extraction algorithm for neural networks with bound decomposition tree

verfasst von: Jia Sheng Heh, Jen Cheng Chen, Maiga Chang

Erschienen in: Neural Computing and Applications | Ausgabe 3/2008

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Abstract

The neural networks are successfully applied to many applications in different domains. However, due to the results made by the neural networks are difficult to explain the decision process of neural networks is supposed as a black box. The explanation of reasoning is important to some applications such like credit approval application and medical diagnosing software. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, a decompositional algorithm is analyzed and designed to extract rules from neural networks. The algorithm is simple but efficient; can reduce the extracted rules but improve the efficiency of the algorithm at the same time. Moreover, the algorithm is compared to the other two algorithms, M-of-N and Garcez, by solving the MONK’s problem.

Vorheriger Artikel Global exponential stability analysis for cellular neural networks with variable coefficients and delays

Nächster Artikel The application of ridge polynomial neural network to multi-step ahead financial time series prediction

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Titel: Designing a decompositional rule extraction algorithm for neural networks with bound decomposition tree
verfasst von: Jia Sheng Heh
Jen Cheng Chen
Maiga Chang
Publikationsdatum: 01.06.2008
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 3/2008
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-007-0115-9

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