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)


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.


Classification Data Mining Nearest Neighbor Particle Swarm Swarm Intelligence 


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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

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