Pattern classification for incomplete data.

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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Official URL: http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arn...

DOI: 10.1109/KES.2000.885854

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)
Subjects:Generalities > Computer Science and Informatics > Artificial Intelligence
Generalities > Computer Science and Informatics
Group:School of Design, Engineering & Computing > Smart Technology Research Centre
ID Code:9647
Deposited By:Professor Bogdan Gabrys
Deposited On:11 Mar 2009 22:59
Last Modified:07 Mar 2013 15:07

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