Rule Extraction from Self-Organizing Networks
نویسندگان
چکیده
Abstract. Generalized relevance learning vector quantization (GRLVQ) [4] constitutes a prototype based clustering algorithm based on LVQ [5] with energy function and adaptive metric. We propose a method for extracting logical rules from a trained GRLVQ-network. Real valued attributes are automatically transformed to symbolic values. The rules are given in the form of a decision tree yielding several advantages: hybrid symbolic/subsymbolic descriptions can be obtained as an alternative and the complexity of the rules can be controlled.
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