Selecting objects for ALVOT

Authors:
Miguel Angel Medina-Pérez;Milton García-Borroto;Yenny Villuendas-Rey;José Ruiz-Shulcloper
Affiliations:
University of Ciego de Ávila, Cuba;Bioplants Center, UNICA, C. de Ávila, Cuba;University of Ciego de Ávila, Cuba;Advanced Technologies Applications Center, MINBAS, Cuba
Venue:
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Year:
2006

Citing 3
Cited 1

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Pattern Recognition Letters
Selecting prototypes in mixed incomplete data

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Nearest prototype classification: clustering, genetic algorithms, or random search?

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

Object selection based on subclass error correcting for ALVOT

CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications

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Abstract

ALVOT is a model of supervised classification based on partial precedences. In this paper a new object selection method based on a voting procedure for ALVOT is proposed. The method was developed for dealing with databases having objects described by features that are not exclusively numeric or categorical. A comparative numerical experiment was performed with different algorithms of object selection. The experimental results show a good performance of the proposed method with respect to the other algorithms.