Abstract
This article describes a new system for learning rules using rotated hyperboxes as individuals of a genetic algorithm (GA). Our method attempts to find out hyperboxes at any orientation by combining deterministic hill-climbing with GA. Standard techniques, such as C4.5, use hyperboxes that are aligned with the coordinate axes. The system uses the decision queue (DQ) as method of representing the rule set. It means that the obtained rules must be applied in specific order, that is, an example will be classify by the i-rule only if it doesn’t satisfy the condition part of the i-1 previous rules. With this policy, the number of rules is less because the rules could be one inside of another one. We have tested our system on real data from UCI repository. Moreover, we have designed some two-dimensional artificial databases to show graphically the experiments. The results are summarized in the last section.
This is a preview of subscription content, log in via an institution to check access.
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
Aguilar, J. and Riquelme, J. COGITO: Un sistema de Autoaprendizaje basado en Algoritmos Genéticos. III Jornadas de Informática, pp. 79–88, Cádiz, 1997.
Blum, A. and Rivest, R. L. Training a 3-node neural network is np-complete. In Proceedengs of the First ADM Workshop on the Computational Learning Theory, pp. 9–18, Cambrige, MA, 1988.
Ghozeil, A. and Fogel, D.B. Discovering Patterns in Spatial Data Using Evolutionary Programming. Genetic Program Conference 96.
Goldberg, D. Genetic Algorithms in search, optimization and machine learning. Addison-Wesley Publishing Company, Inc. 1989.
Michalewicz, Z. Genetic Algorithms + Data Structures = Evolution Programs. Second Edition, Springer-Verlag, 1994.
Mitchell, T. Machine Learning. MacGraw-Hill, 1997.
Murphy, P. and Aha, D.W. UCI Repository of Machine Learning Databases. Dept. of Information and Computer Science. University of California, Irvine, 1994.
Murthy, S. K., Kasif, S. and Salzberg, S. A system for induction of oblique decision trees. Journal of Artificial Intelligence Research, 1994. Morgan Kaufmann Publishers.
Quinlan, J. R. C4.5: programs for Machine Learning. Morgan Kaufmann Pub., 1993.
Riquelme J. and Aguilar, J. COGITO 2.0: Una herramienta para obtener un Clasificador Jerárquico en Apredizaje Supervisado. Conferencia de la Asociación Española para la Inteligencia Artificial. CAEPIA’97. pp. 489–498, Málaga, 1997.
Rivest, R.L. Learning Decision Lists. Machine Learning, 87. pp. 229–246.
Simpson, P.K. Fuzzy Min-Max Neural Networks. II. Clustering. IEEE Trans. Fuzzy Systems, Vol. 1:1, 32–45.
Venturini, G. SIA: a Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aguilar, J., Riquelme, J., Toro, M. (1998). Decision Queue Classifier for Supervised Learning Using Rotated Hyperboxes. In: Coelho, H. (eds) Progress in Artificial Intelligence — IBERAMIA 98. IBERAMIA 1998. Lecture Notes in Computer Science(), vol 1484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49795-1_29
Download citation
DOI: https://doi.org/10.1007/3-540-49795-1_29
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64992-2
Online ISBN: 978-3-540-49795-0
eBook Packages: Springer Book Archive