Abstract
Particle Swarm Optimization (PSO) is a heuristic optimization technique showing relationship with Evolutionary Algorithms and strongly based on the concept of swarm. It is used in this paper to face the problem of classification of instances in multiclass databases. Only a few papers exist in literature in which PSO is tested on this problem and there are no papers showing a thorough comparison for it against a wide set of techniques typically used in the field. Therefore in this paper PSO performance is compared on nine typical test databases against those of nine classification techniques widely used for classification purposes. PSO is used to find the optimal positions of class centroids in the database attribute space, via the examples contained in the training set. Performance of a run, instead, is computed as the percentage of instances of testing set which are incorrectly classified by the best individual achieved in the run. Results show the effectiveness of PSO, which turns out to be the best on three out of the nine challenged problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Eberhart, R.C., Kennedy, J.: A new optimizer using particle swarm theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, pp. 39–43. IEEE Press, Piscataway (1995)
Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks IV, pp. 1942–1948. IEEE Press, Piscataway (1995)
Han, J., Kamber, M.: Data Mining: Concept and Techniques. Morgan Kaufmann, San Francisco (2001)
Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. The MIT Press, Cambridge (2001)
Sousa, T., Silva, A., Neves, A.: Particle swarm based data mining algorithms for classification tasks. Parallel Computing 30, 767–783 (2004)
van der Merwe, D.W., Engelbrecht, A.P.: Data clustering using particle swarm optimization. In: Proceedings of the IEEE Congress on Evolutionary Computation. IEEE Press, Piscataway (2003)
Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison–Wesley, Reading (1989)
Fogel, L.J., Marsh, A.J., Walsh, M.J.: Artificial Intelligence through Simulated Evolution. Wiley & Sons, New York (1966)
Shi, Y., Eberhart, R.C.: A modified particle swarm optimizer. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 69–73. IEEE Press, Piscataway (1998)
Blake, C.L., Merz, C.J.: UCI Repository of Machine Learning Databases, University of California, Irvine (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tool and Technique with Java Implementation. Morgan Kaufmann, San Francisco (2000)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning representation by back–propagation errors. Nature 323, 533–536 (1986)
Hassoun, M.H.: Fundamentals of Artificial Neural Networks. The MIT Press, Cambridge (1995)
Cleary, J.G., Trigg, L.E.: K *: An instance–based learner using an entropic distance measure. In: Proceedings of the 12th International Conference on Machine Learning, pp. 108–114 (1995)
Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)
Webb, G.I.: Multiboosting: a technique for combining boosting and wagging. Machine Learning 40, 159–196 (2000)
Kohavi, R.: Scaling up the accuracy of naive-bayes classifiers: a decision tree hybrid. In: Proceedings of the Second Internarional Conference on Knowledge Discovery and Data Mining, pp. 202–207 (1996)
Compton, P., Jansen, R.: Knowledge in context: a strategy for expert system maintenance. In: Proceedings of Artificial Intelligence, pp. 292–306. Springer, Berlin (1988)
Demiroz, G., Guvenir, A.: Classification by voting feature intervals. In: van Someren, M., Widmer, G. (eds.) ECML 1997. LNCS, vol. 1224, pp. 85–92. Springer, Heidelberg (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
De Falco, I., Della Cioppa, A., Tarantino, E. (2006). Evaluation of Particle Swarm Optimization Effectiveness in Classification. In: Bloch, I., Petrosino, A., Tettamanzi, A.G.B. (eds) Fuzzy Logic and Applications. WILF 2005. Lecture Notes in Computer Science(), vol 3849. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11676935_20
Download citation
DOI: https://doi.org/10.1007/11676935_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32529-1
Online ISBN: 978-3-540-32530-7
eBook Packages: Computer ScienceComputer Science (R0)