An Adaptive Michigan Approach PSO for Nearest Prototype Classification

  • Alejandro Cervantes
  • Inés Galván
  • Pedro Isasi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)

Abstract

Nearest Prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. In this paper we develop a new algorithm (called AMPSO), based on the Particle Swarm Optimization (PSO) algorithm, that can be used to find those prototypes. Each particle in a swarm represents a single prototype in the solution; the swarm evolves using modified PSO equations with both particle competition and cooperation. Experimentation includes an artificial problem and six common application problems from the UCI data sets. The results show that the AMPSO algorithm is able to find solutions with a reduced number of prototypes that classify data with comparable or better accuracy than the 1-NN classifier. The algorithm can also be compared or improves the results of many classical algorithms in each of those problems; and the results show that AMPSO also performs significantly better than any tested algorithm in one of the problems.

Keywords

Classification Data Mining Nearest Neighbor Particle Swarm Swarm Intelligence 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data mining and knowledge discovery 6(2), 153–172 (2002)CrossRefMathSciNetMATHGoogle Scholar
  2. 2.
    Fernández, F., Isasi, P.: Evolutionary design of nearest prototype classifiers. Journal of Heuristics 10(4), 431–454 (2004)CrossRefGoogle Scholar
  3. 3.
    Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann Publishers, San Francisco (2001)Google Scholar
  4. 4.
    Holland, J.H.: Adaptation. Progress in theoretical biology, 263–293 (1976)Google Scholar
  5. 5.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar
  6. 6.
    Cervantes, A., Isasi, P., Galván, I.: A comparison between the pittsburgh and michigan approaches for the binary pso algorithm. In: Proceedings of the 2005 IEEE Congress on Evolucionary Computation, CEC 2005, pp. 290–297. IEEE Computer Society Press, Los Alamitos (2005)CrossRefGoogle Scholar
  7. 7.
    Blackwell, T.M., Bentley, P.J.: Don’t push me! collision-avoiding swarms. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC), pp. 1691–1696. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  8. 8.
    Blackwell, T.M., Bentley, P.J.: Dynamic search with charged swarms. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002), pp. 19–26 (2002)Google Scholar
  9. 9.
    Cervantes, A., Isasi, P., Galván, I.: Binary particle swarm optimization in classification. Neural Network World 15(3), 229–241 (2005)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Alejandro Cervantes
    • 1
  • Inés Galván
    • 1
  • Pedro Isasi
    • 1
  1. 1.Department of Computer Science, University Carlos III de Madrid, Avda. Universidad, 30. 28911 Leganés, MadridSpain

Personalised recommendations