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ToolsGabrys, B., 2000. Pattern classification for incomplete data. In: Knowledge-Based Intelligent Engineering Systems and Allied Technologies, 2000. Proceedings. Fourth International Conference on, 30 August -1 September 2000, Brighton, England, 454-457.
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Abstract
The problem of pattern classification for inputs with missing values is considered. A general fuzzy min-max (GFMM) neural network utilising hyperbox fuzzy sets as a representation of data cluster prototypes is used. It is shown how a classification decisions can be carried out on a subspace of high dimensional input data. No substitution scheme for missing values is utilised. The result is a classification procedure that reduces a number of viable class alternatives on the basis of available information rather than attempting to produce one winning class without supporting evidence. A number of simulation results for well known data sets are provided to illustrate the properties and performance of the proposed approach.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Group: | Faculty of Science & Technology |
| ID Code: | 9647 |
| Deposited By: | Professor Bogdan Gabrys LEFT |
| Deposited On: | 11 Mar 2009 22:59 |
| Last Modified: | 14 Mar 2022 13:21 |
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