Experimental investigation on editing for the k-NN rule through a genetic algorithm

  • Ludmila I. Kuncheva
  • Yordan K. Yotzov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 970)


An experimental investigation on selection of a reference set for the k-Nearest Neighbors (k-NN) classification method has been conducted. Genetic algorithms have been employed bringing together the strategy to preserve the decision boundary and that of selecting the most ”typical” objects as prototypes. The chromosome is directly mapped onto the reference set and the best subset is subsequently evolved. Two fitness functions have been examined. The results are contrasted with those obtained with the whole sample (before editing), Hart's and Wilson's methods. Independent subsets have been used for training and for test. Two data sets were used: two highly overlapping Gaussian classes and a data set from neonatology. The results with the proposed editing technique compare favorably with those obtained with the classical methods and with the non-edited sample.


Pattern recognition Genetic algorithms Editing strategies k-Nearest Neighbors (k-NN) rule 


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  1. 1.
    Dasarathy B.V. (1990) Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques, IEEE Computer Society Press, Los Alamitos, Calofornia, 1990.Google Scholar
  2. 2.
    Devijver P., J. Kittler (1982) Pattern Recognition. A statistical approach, Prentice Hall Int.Google Scholar
  3. 3.
    Hart P.E. (1968) The condensed nearest neighbor rule, IEEE Transactions on Information Theory, IT-16, 515–516.Google Scholar
  4. 4.
    Kuncheva L.I. (1994) Selection of a k-NN reference set by genetic algorithm and index of fuzziness, Proc. Second European Conference on Fuzzy and Intelligent Technologies, EUFIT'94, Aachen, Germany, 640–644.Google Scholar
  5. 5.
    Kuncheva L.I. (1994) Editing for the k-nearest neighbors rile by a genetic algorithm, Pattern Recognition Letters, to appear.Google Scholar
  6. 6.
    Wilson D.L. (1972) Asymptotic properties of nearest neighbor rules using edited data, IEEE Transactions on Systems, Man, and Cybernetics, SMC-2, 408–421.Google Scholar
  7. 7.
    Yang M.-S., C.-T. Chen. (1993) On strong consistency of the fuzzy generalized nearest neighbor rule, Fuzzy Sets and Systems, 60, 273–281.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Ludmila I. Kuncheva
    • 1
  • Yordan K. Yotzov
    • 2
  1. 1.Department of Biomedical EngineeringBulgarian Academy of SciencesSofiaBulgaria
  2. 2.Department of Computer SciencesNew Bulgarian UniversitySofiaBulgaria

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