Abstract
Decision tree induction has been widely used to generate classifiers from training data through a process of recursively splitting the data space. In the case of training on continuous-valued data, the associated attributes must be discretized in advance or during the learning process. The commonly used method is to partition the attribute range into two or several intervals using single or a set of cut points. One inherent disadvantage in these methods is that the use of sharp cut points makes the induced decision trees sensitive to noise. To overcome this problem this paper presents an alternative method called adaptive discretization based on Ant Colony Decision Tree (ACDT) approach. Experimental results showed that, by using that methodology, better classification accuracy has been obtained in both training and testing data sets in majority of cases concerning the classical decision tree constructed by ants. It suggests that the robustness of decision trees could be improved by means of this approach.
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.
Similar content being viewed by others
References
Boryczka, U., Kozak, J.: Ant colony decision trees – a new method for constructing decision trees based on ant colony optimization. In: Pan, J.S., Chen, S.M., Nguyen, N. (eds.) ICCCI 2010. LNCS, vol. 6421, pp. 373–382. Springer, Heidelberg (2010)
Boryczka, U., Kozak, J.: A New Heuristic Function in Ant–Miner Approach. In: ICEIS 2009, Milan, Italy, pp. 33–38 (2009)
Boryczka, U., Kozak, J.: New Algorithms for Generation Decision Trees – Ant–Miner and Its Modifications, pp. 229–264. Springer, Berlin (2009)
Boryczka, U., Kozak, J., Skinderowicz, R.: Parellel Ant–Miner. Parellel implementation of an ACO techniques to discover classification rules with OpenMP. In: MENDEL 2009, pp. 197–205. University of Technology, Brno (2009)
Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Chapman & Hall, New York (1984)
Dorigo, M., Gambardella, L.M.: Ant Colony System: A cooperative learning approach to the Traveling Salesman Problem. IEEE Tr. Evol. Comp. 1, 53–66 (1997)
Dorigo, M., Birattari, M., Stützle, T., Libre, U., Bruxelles, D., Roosevelt, A.F.D.: Ant colony optimization – artificial ants as a computational intelligence technique. IEEE Comput. Intell. Mag. 1, 28–39 (2006)
Otero, F., Freitas, A., Johnson, C.: cAnt-Miner: An ant colony classification algorithm to cope with continuous attributes. In: Dorigo, M., Birattari, M., Blum, C., Clerc, M., Stützle, T., Winfield, A.F.T. (eds.) ANTS 2008. LNCS, vol. 5217, pp. 48–59. Springer, Heidelberg (2008)
Otero, F.E.B., Freitas, A.A., Johnson, C.G.: Handling continuous attributes in ant colony classification algorithms. In: CIDM, pp. 225–231 (2009)
Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: An ant colony algorithm for classification rule discovery. In: Abbas, H., Sarker, R., Newton, C. (eds.) Data Mining: a Heuristic Approach, Idea Group Publishing, London (2002)
Parpinelli, R.S., Lopes, H.S., Freitas, A.A.: Data mining with an ant colony optimization algorithm. IEEE Transactions on Evolutionary Computation, Special issue on Ant Colony Algorithms, 321–332 (2004)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
Quinlan, J.R.: Improved use of continuous attributes in c4.5. Journal of Artificial Intelligence Research 4, 77–90 (1996)
Rokach, L., Maimon, O.: Data Mining With Decision Trees: Theory and Applications. World Scientific Publishing, Singapore (2008)
Schaefer, G.: Ant colony optimisation classification for gene expression data analysis. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds.) RSFDGrC 2009. LNCS, vol. 5908, pp. 463–469. Springer, Heidelberg (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Boryczka, U., Kozak, J. (2011). An Adaptive Discretization in the ACDT Algorithm for Continuous Attributes. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6923. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23938-0_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-23938-0_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23937-3
Online ISBN: 978-3-642-23938-0
eBook Packages: Computer ScienceComputer Science (R0)