Abstract
In Neural Networks models the knowledge synthesized from the training process is represented in a subsymbolic fashion (weights, kernels, combination of numerical descriptions) that makes difficult its interpretation. The interpretation of the internal representation of a successful Neural Network can be useful to understand the nature of the problem and its solution, to use the Neural “model” as a tool that gives insights about the problem solved and not just as a solving mechanism treated as a black box. The internal representation used by the family of kernel-based Neural Networks (including Radial Basis Functions, Support Vector machines, Coulomb potential methods, and some probabilistic Neural Networks) can be seen as a set of positive instances of classification and, thereafter, used to derive fuzzy rules suitable for explanation or inference processes. The probabilistic nature of the kernel-based Neural Networks is captured by the membership functions associated to the components of the rules extracted. In this work we propose a method to extract fuzzy rules from trained Neural Networks of the family mentioned; comparing the quality of the knowledge extracted by different methods using known machine learning benchmarks.
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© 1999 Springer-Verlag Berlin Heidelberg
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Ramírez, J.M. (1999). Extracting rules from artificial neural networks with kernel-based representations. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100473
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DOI: https://doi.org/10.1007/BFb0100473
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