Object selection based on subclass error correcting for ALVOT

Authors:
Miguel Angel Medina-Pérez;Milton García-Borroto;José Ruiz-Shulcloper
Affiliations:
University of Ciego de Ávila, Cuba;Bioplants Center, UNICA, C. de Ávila, Cuba;Advanced Technologies Applications Center, MINBAS, Cuba
Venue:
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Year:
2007

Citing 4
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Abstract

ALVOT is a supervised classification model based on partial precedences. These classifiers work with databases having objects described simultaneously by numeric and nonnumeric features. In this paper a new object selection method based on the error per subclass is proposed for improving the accuracy, especially with noisy training matrixes. A comparative numerical experiment was performed with different methods of object selection. The experimental results show a good performance of the proposed method with respect to previously reported in the literature.