Selecting Prototypes in Mixed Incomplete Data

  • Milton García-Borroto
  • José Ruiz-Shulcloper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


In this paper we introduce a new method for selecting prototypes with Mixed Incomplete Data (MID) object description, based on an extension of the Nearest Neighbor rule. This new rule allows dealing with functions that are not necessarily dual functions of distances. The introduced compact set editing method (CSE) constructs a prototype consistent subset, which is also subclass consistent. The experimental results show that CSE has a very nice computational behavior and effectiveness, reducing around 50% of prototypes without appreciable degradation on accuracy, in almost all databases with more than 300 objects.


Object Description Similar Neighbor Neighbor Rule Prototype Selection Proximity Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Milton García-Borroto
    • 1
  • José Ruiz-Shulcloper
    • 2
  1. 1.Bioplants Center, UNICA, C. de ÁvilaCuba
  2. 2.Advanced Technologies Applications Center, MINBASCuba

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