Publikation: Visualizing High Dimensional Fuzzy Rules
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In this paper we present an approach to visualize a potentially high-dimensional and large number of (fuzzy) rules in two dimensions. This visualization presents the entire set of rules to the user as one coherent picture. We use a gradient descent based algorithm to generate a 2D-view of the rule set which minimizes the error on the pair-wise fuzzy distances between all rules. This approach is superior to a simple projection and also most non-linear transformations in that it concentrates on the important feature, that is the inter-point distances. In order to make use of the uncertain nature of the underlying fuzzy rules, a new fuzzy distance-measure was developed. The visualizations of a rule set for the well-known IRIS dataset as well as fuzzy models for other benchmark data sets are illustrated and discussed.
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HOLVE, Rainer, Michael R. BERTHOLD, 2000. Visualizing High Dimensional Fuzzy Rules. In: Computational intelligence im industriellen Einsatz : Fuzzy Systeme, neuronale Netze, evolutionäre Algorithmen, Data mining ; Tagung Baden-Baden, 11. und 12. Mai 2000. Düsseldorf: VDI Verein Deutscher Ingenieure, 2000, pp. 21-25. ISBN 3-18-091526-9BibTex
@inproceedings{Holve2000Visua-24321, year={2000}, title={Visualizing High Dimensional Fuzzy Rules}, isbn={3-18-091526-9}, publisher={VDI Verein Deutscher Ingenieure}, address={Düsseldorf}, booktitle={Computational intelligence im industriellen Einsatz : Fuzzy Systeme, neuronale Netze, evolutionäre Algorithmen, Data mining ; Tagung Baden-Baden, 11. und 12. Mai 2000}, pages={21--25}, author={Holve, Rainer and Berthold, Michael R.} }
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