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Obtaining Classification Rules Using lvqPSO

  • Laura Lanzarini
  • Augusto Villa Monte
  • Germán Aquino
  • Armando De Giusti
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9140)

Abstract

Technological advances nowadays have made it possible for processes to handle large volumes of historic information whose manual processing would be a complex task. Data mining, one of the most significant stages in the knowledge discovery and data mining (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called lvqPSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with several methods proposed by other authors and measured over 15 databases, and satisfactory results were obtained.

Keywords

Classification rules Data mining Adaptive strategies Particle swarm optimization Learning vector quantization 

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References

  1. 1.
    Chen, M., Ludwig, S.: Discrete particle swarm optimization with local search strategy for rule classification. In: 2012 Fourth World Congress on Nature and Biologically Inspired Computing (NaBIC), pp. 162–167 (2012)Google Scholar
  2. 2.
    Frank, E., Witten, I.H.: Generating accurate rule sets without global optimization. In: Proceedings of the Fifteenth International Conference on Machine Learning, ICML 1998, pp. 144–151. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (1998)Google Scholar
  3. 3.
    Holden, N., Freitas, A.A.: A hybrid pso/aco algorithm for discovering classification rules in data mining. Journal of Artificial Evolution and Applications 2008, 2:1–2:11 (2008)CrossRefGoogle Scholar
  4. 4.
    Hung, C., Huang, L.: Extracting rules from optimal clusters of self-organizing maps. In: Second International Conference on Computer Modeling and Simulation, ICCMS 2010, vol. 1, pp. 382–386 (2010)Google Scholar
  5. 5.
    Jiang, Y., Wang, L., Chen, L.: A hybrid dynamical evolutionary algorithm for classification rule discovery. In: Second International Symposium on Intelligent Information Technology Application, vol. 3, pp. 76–79 (2008)Google Scholar
  6. 6.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  7. 7.
    Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104–4108. IEEE Computer Society, Washington, DC, USA (1997)Google Scholar
  8. 8.
    Khan, N., Iqbal, M., Baig, A.: Data mining by discrete pso using natural encoding. In: 2010 5th International Conference on Future Information Technology (FutureTech), pp. 1–6 (2010)Google Scholar
  9. 9.
    Kohonen, T.: The self-organizing map. Proceedings of the IEEE 78(9), 1464–1480 (1990)CrossRefGoogle Scholar
  10. 10.
    Kohonen, T., Schroeder, M.R., Huang, T.S. (eds.): Self-Organizing Maps, 3rd edn. Springer, New York (2001)zbMATHGoogle Scholar
  11. 11.
    Lanzarini, L., Monte, A.V., Ronchetti, F.: Som+pso. a novel method to obtain classification rules. Journal of Computer Science & Technology 15(1), 15–22 (2015)Google Scholar
  12. 12.
    Lanzarini, L., Leza, V., De Giusti, A.: Particle swarm optimization with variable population size. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 438–449. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  13. 13.
    Lanzarini, L., López, J., Maulini, J.A., De Giusti, A.: A new binary PSO with velocity control. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds.) ICSI 2011, Part I. LNCS, vol. 6728, pp. 111–119. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  14. 14.
    Medland, M., Otero, F.E.B., Freitas, A.A.: Improving the cAnt-Miner\(_\text{ PB }\) classification algorithm. In: Dorigo, M., Birattari, M., Blum, C., Christensen, A.L., Engelbrecht, A.P., Groß, R., Stützle, T. (eds.) ANTS 2012. LNCS, vol. 7461, pp. 73–84. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  15. 15.
    Özçift, A., Kaya, M., Gülten, A., Karabulut, M.: Swarm optimized organizing map (swom): A swarm intelligence based optimization of self-organizing map. Expert Systems with Applications 36(7), 10640–10648 (2009)CrossRefGoogle Scholar
  16. 16.
    Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993) Google Scholar
  17. 17.
    UCI: Machine learning repository. http://archive.ics.uci.edu/ml
  18. 18.
    Venturini, G.: Sia: a supervised inductive algorithm with genetic search for learning attributes based concepts. In: Brazdil, P.B. (ed.) ECML-93. LNCS, vol. 667, pp. 280–296. Springer, Berlin Heidelberg (1993)CrossRefGoogle Scholar
  19. 19.
    Wang, H., Zhang, Y.: Improvement of discrete particle swarm classification system. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 2, pp. 1027–1031 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Laura Lanzarini
    • 1
  • Augusto Villa Monte
    • 1
  • Germán Aquino
    • 1
  • Armando De Giusti
    • 1
  1. 1.Institute of Research in Computer Science LIDI (III-LIDI), Faculty of Computer ScienceNational University of La Plata (UNLP)La PlataArgentina

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