Knowledge Extraction from Unsupervised Multi-topographic Neural Network Models | Zendy

Shadi Al Shehabi | Zendy; Jean-Charles Lamirel | Zendy

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Knowledge Extraction from Unsupervised Multi-topographic Neural Network Models

Author(s) -

Shadi Al Shehabi,

Jean-Charles Lamirel

Publication year - 2005

Publication title -

lecture notes in computer science

Language(s) - English

Resource type - Book series

SCImago Journal Rank - 0.249

H-Index - 400

eISSN - 1611-3349

pISSN - 0302-9743

DOI - 10.1007/11550907_75

Subject(s) - computer science , artificial neural network , classifier (uml) , knowledge extraction , data mining , artificial intelligence , generalization , scope (computer science) , machine learning , pattern recognition (psychology) , mathematics , mathematical analysis , programming language

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

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