Gabrys, 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, pp. 454-457.
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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)|
|Subjects:||Generalities > Computer Science and Informatics > Artificial Intelligence|
Generalities > Computer Science and Informatics
|Group:||School of Design, Engineering & Computing|
|Deposited By:||Professor Bogdan Gabrys|
|Deposited On:||11 Mar 2009 22:59|
|Last Modified:||07 Mar 2013 15:07|
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