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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Brighton, H., Mellish, C.: Advances in instance selection for instance-based learning algorithms. Data mining and knowledge discovery 6(2), 153–172 (2002)
Fernández, F., Isasi, P.: Evolutionary design of nearest prototype classifiers. Journal of Heuristics 10(4), 431–454 (2004)
Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann Publishers, San Francisco (2001)
Holland, J.H.: Adaptation. Progress in theoretical biology, 263–293 (1976)
Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)
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)
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)
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)
Cervantes, A., Isasi, P., Galván, I.: Binary particle swarm optimization in classification. Neural Network World 15(3), 229–241 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Cervantes, A., Galván, I., Isasi, P. (2007). An Adaptive Michigan Approach PSO for Nearest Prototype Classification. In: Mira, J., Álvarez, J.R. (eds) Nature Inspired Problem-Solving Methods in Knowledge Engineering. IWINAC 2007. Lecture Notes in Computer Science, vol 4528. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73055-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-540-73055-2_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73054-5
Online ISBN: 978-3-540-73055-2
eBook Packages: Computer ScienceComputer Science (R0)