Towards Scalable Prototype Selection by Genetic Algorithms with Fast Criteria

  • Yenisel Plasencia-Calaña
  • Mauricio Orozco-Alzate
  • Heydi Méndez-Vázquez
  • Edel García-Reyes
  • Robert P. W. Duin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8621)


How to select the prototypes for classification in the dissimilarity space remains an open and interesting problem. Especially, achieving scalability of the methods is desirable due to enormous amounts of information arising in many fields. In this paper we pose the question: are genetic algorithms good for scalable prototype selection? We propose two methods based on genetic algorithms, one supervised and the other unsupervised, whose analyses provide an answer to the question. Results on dissimilarity datasets show the effectiveness of the proposals.


dissimilarity space scalable prototype selection genetic algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Yenisel Plasencia-Calaña
    • 1
    • 2
  • Mauricio Orozco-Alzate
    • 3
  • Heydi Méndez-Vázquez
    • 1
  • Edel García-Reyes
    • 1
  • Robert P. W. Duin
    • 2
  1. 1.Advanced Technologies Application CenterHavanaCuba
  2. 2.Faculty of Electrical Engineering, Mathematics and Computer SciencesDelft University of TechnologyThe Netherlands
  3. 3.Departamento de Informática y ComputaciónUniversidad Nacional de Colombia - Sede ManizalesManizalesColombia

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