Object Selection Based on Subclass Error Correcting for ALVOT

  • Miguel Angel Medina-Pérez
  • Milton García-Borroto
  • José Ruiz-Shulcloper
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


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.


Partial precedence mixed and incomplete data editing method 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Miguel Angel Medina-Pérez
    • 1
  • Milton García-Borroto
    • 2
  • José Ruiz-Shulcloper
    • 3
  1. 1.University of Ciego de ÁvilaCuba
  2. 2.Bioplants Center, UNICA, C. de ÁvilaCuba
  3. 3.Advanced Technologies Applications Center, MINBASCuba

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