Knowledge extraction from unsupervised multi-topographic neural network models

Authors:
Shadi Al Shehabi;Jean-Charles Lamirel
Affiliations:
Loria, Vandoeuvre-lès-Nancy Cedex, France;Loria, Vandoeuvre-lès-Nancy Cedex, France
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

This paper presents a new approach whose aim is to extent the scope of numerical models by providing them with knowledge extraction capabilities. The basic model which is considered in this paper is a multi-topographic neural network model. One of the most powerful features of this model is its generalization mechanism that allows rule extraction to be performed. The extraction of association rules is itself based on original quality measures which evaluate to what extent a numerical classification model behaves as a natural symbolic classifier such as a Galois lattice. A first experimental illustration of rule extraction on documentary data constituted by a set of patents issued form a patent database is presented.