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A Nearest Features Classifier Using a Self-organizing Map for Memory Base Evaluation

  • Christos Pateritsas
  • Andreas Stafylopatis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)

Abstract

Memory base learning is one of main fields in the area of machine learning. We propose a new methodology for addressing the classification task that relies on the main idea of the k – nearest neighbors algorithm, which is the most important representative of this field. In the proposed approach, given an unclassified pattern, a set of neighboring patterns is found, but not necessarily using all input feature dimensions. Also, following the concept of the naïve Bayesian classifier, we adopt the hypothesis of the independence of input features in the outcome of the classification task. The two concepts are merged in an attempt to take advantage of their good performance features. In order to further improve the performance of our approach, we propose a novel weighting scheme of the memory base. Using the self-organizing maps model during the execution of the algorithm, dynamic weights of the memory base patterns are produced. Experimental results have shown superior performance of the proposed method in comparison with the aforementioned algorithms and their variations.

Keywords

Classification Task Weighting Scheme Near Neighbor Data Pattern Evaluation Factor 
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 2006

Authors and Affiliations

  • Christos Pateritsas
    • 1
  • Andreas Stafylopatis
    • 1
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensZografou, AthensGreece

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