WebDR: A Web Workbench for Data Reduction

  • Stefanos Ougiaroglou
  • Georgios Evangelidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8726)


Data reduction is a common preprocessing task in the context of the k nearest neighbour classification. This paper presents WebDR, a web-based application where several data reduction techniques have been integrated and can be executed on-line. WebDR allows the performance evaluation of the classification process through a web interface. Therefore, it can be used by the academia for educational and experimental purposes.


k-NN classification data reduction web-based application 


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  1. 1.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-based learning algorithms. Mach. Learn. 6(1), 37–66 (1991), Google Scholar
  2. 2.
    Alcala-Fdez, J., Sanchez, L., Garcia, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fernandez, J.C., Herrera, F.: Keel: a software tool to assess evolutionary algorithms for data mining problems. Soft Comput. 13(3), 307–318 (2008)CrossRefGoogle Scholar
  3. 3.
    Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Press (1991)Google Scholar
  4. 4.
    Dasarathy, B.V., Sánchez, J.S., Townsend, S.: Nearest neighbour editing and condensing tools synergy exploitation. Pattern Analysis & Applications 3(1), 19–30 (2000)CrossRefGoogle Scholar
  5. 5.
    Devijver, P.A., Kittler, J.: On the edited nearest neighbor rule. In: Proceedings of the Fifth International Conference on Pattern Recognition. The Institute of Electrical and Electronics Engineers (1980)Google Scholar
  6. 6.
    Garcia, S., Derrac, J., Cano, J., Herrera, F.: Prototype selection for nearest neighbor classification: Taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34(3), 417–435 (2012)CrossRefGoogle Scholar
  7. 7.
    Hart, P.E.: The condensed nearest neighbor rule. IEEE Transactions on Information Theory 14(3), 515–516 (1968)CrossRefGoogle Scholar
  8. 8.
    Olvera-Lopez, J.A., Carrasco-Ochoa, J.A., Trinidad, J.F.M.: A new fast prototype selection method based on clustering. Pattern Anal. Appl. 13(2), 131–141 (2010)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Ougiaroglou, S., Evangelidis, G.: Efficient editing and data abstraction by finding homogeneous clusters. In: Submitted, under reviewGoogle Scholar
  10. 10.
    Ougiaroglou, S., Evangelidis, G.: RHC: Non-parametric cluster-based data reduction for efficient k-nn classification. Pattern Analysis and Applications pp. (accepted, to appear)Google Scholar
  11. 11.
    Ougiaroglou, S., Evangelidis, G.: Efficient dataset size reduction by finding homogeneous clusters. In: Proceedings of the Fifth Balkan Conference in Informatics, BCI 2012, pp. 168–173. ACM Press, New York (2012)Google Scholar
  12. 12.
    Ougiaroglou, S., Evangelidis, G.: A simple noise-tolerant abstraction algorithm for fast k-NN classification. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012, Part II. LNCS, vol. 7209, pp. 210–221. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Ougiaroglou, S., Evangelidis, G.: AIB2: An abstraction data reduction technique based on ib2. In: Proceedings of the 6th Balkan Conference in Informatics, BCI 2013, pp. 13–16. ACM, New York (2013)Google Scholar
  14. 14.
    Ougiaroglou, S., Evangelidis, G.: EHC: Non-parametric editing by finding homogeneous clusters. In: Beierle, C., Meghini, C. (eds.) FoIKS 2014. LNCS, vol. 8367, pp. 290–304. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  15. 15.
    Sánchez, J.S.: High training set size reduction by space partitioning and prototype abstraction. Pattern Recognition 37(7), 1561–1564 (2004)CrossRefGoogle Scholar
  16. 16.
    Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics 6, 448–452 (1976)CrossRefzbMATHMathSciNetGoogle Scholar
  17. 17.
    Triguero, I., Derrac, J., Garcia, S., Herrera, F.: A taxonomy and experimental study on prototype generation for nearest neighbor classification. Trans. Sys. Man Cyber Part C 42(1), 86–100 (2012)CrossRefGoogle Scholar
  18. 18.
    Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans. on Systems, Man, and Cybernetics 2(3), 408–421 (1972)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Stefanos Ougiaroglou
    • 1
  • Georgios Evangelidis
    • 1
  1. 1.Department of Applied Informatics, School of Information SciencesUniversity of MacedoniaThessalonikiGreece

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