Abstract
This paper investigates a new model that takes advantage of the cooperative self-organization of Ant Algorithms to evolve a naturally inspired pattern recognition (and also clustering) method. The approach considers each data item as an ant that changes the environment as it moves through it. The algorithm is successfully applied to well-known classification problems and yields better results than some other classification approaches, like K-Nearest Neighbours and Neural Networks.
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
Labroche, N., Monmarche, N., Venturini, G.: AntClust: Ant Clustering and Web Usage Mining. In: GECCO 2003. LNCS, vol. 2723, pp. 25–36. Springer, Heidelberg (2003)
Deneubourg, J., Goss, S., Franks, N., Sendova-Franks, A., Detrain, C., Chrétien, L.: The dynamics of collective sorting robot-like ants and ant-like robots. In: Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats table of contents, pp. 356–363 (1991)
Bonabeau, E., Theraulaz, G., Fourcassié, V., Deneubourg, J.: Phase-ordering kinetics of cemetery organization in ants. Physical Review E 57(4), 4568–4571 (1998)
Ramos, V., Merelo, J.J.: Self-organized stigmergic document maps: Environment as a mechanism for context learning. In: Alba, E., Fernández, F., Gómez, J.A., Herrera, F., Hidalgo, J.I., Merelo-Guervós, J.J., Sánchez, J.M. (eds.) Actas primer congreso español algoritmos evolutivos, AEB 2002, pp. 284–293. Universidad de Extremadura (2002), http://citeseer.nj.nec.com/ramos02selforganized.html
Kohonen, T.: The Self-Organizing Maps. Springer, Heidelberg (2001)
Chialvo, D., Millonas, M.: How swarms build cognitive maps. In: The Biology and Technology of Intelligent Autonomous Agents. NATO ASI Series, vol. 144, pp. 439–450 (1995)
Wilson, E.: The Insect Societies. Belknam Press, Cambridge (1971)
Fix, E., Hodges, J.L.: Discriminatory analysis: Nonparametric discrimination: Consistency properties. In: International Statistical Review, vol. 57, pp. 238–247 (1989)
Friedman, J.H.: Regularized discriminant analysis. Journal of the American Statistical Association 84, 165–175 (1989)
Moller, F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Networks 6, 525–533 (1990)
Widrow, B., Lehr, M.: 30 years of adaptive neural networks: Peceptron, madaline, and backpropagation. In: Proceedings of the IEEE, vol. 78, pp. 1415–1442 (1990)
Fernandes, C., Ramos, V., Rosa, A.C.: Varying the population size of artificial foraging swarms on time varying landscapes. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 311–316. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Fernandes, C., Mora, A.M., Merelo, J.J., Ramos, V., Laredo, J.L., Rosa, A. (2008). KANTS: Artifical Ant System for Classification. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2008. Lecture Notes in Computer Science, vol 5217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87527-7_35
Download citation
DOI: https://doi.org/10.1007/978-3-540-87527-7_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87526-0
Online ISBN: 978-3-540-87527-7
eBook Packages: Computer ScienceComputer Science (R0)