Improving Accuracy of LVQ Algorithm by Instance Weighting

  • Marcin Blachnik
  • Włodzisław Duch
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6354)


Similarity-based methods belong to the most accurate data mining approaches. A large group of such methods is based on instance selection and optimization, with Learning Vector Quantization (LVQ) algorithm being a prominent example. Accuracy of LVQ highly depends on proper initialization of prototypes and the optimization mechanism. Prototype initialization based on context dependent clustering is introduced, and modification of the LVQ cost function that utilizes additional information about class-dependent distribution of training vectors. The new method is illustrated on 6 benchmark datasets, finding simple and accurate models of data in form of prototype-based rules.


Radial Basis Function Network Benchmark Dataset Learn Vector Quantization Wisconsin Breast Cancer Instance Weighting 
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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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Marcin Blachnik
    • 1
  • Włodzisław Duch
    • 2
  1. 1.Department of Management and InformaticsSilesian University of TechnologyKrasinskiego 8Poland
  2. 2.Department of InformaticsNicolaus Copernicus UniversityToruńPoland

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