Abstract
As raw data become available in ever-increasing amounts, there is a need for automated methods that extract comprehensible knowledge from the data. In our previous work we have applied evolutionary algorithms to the problem of mining predictive rules from time series. In this paper we investigate the effect of discretization on the predictive power of the evolved rules. We compare the effects of using simple model selection based on validation performance, majority vote ensembles, and naive Bayesian combination of classifiers.
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
Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)
Domingos, P., Pazzani, M.J.: On the optimality of the simple bayesian classifier under zero-one loss. Machine Learning 29(2–3), 103–130 (1997)
Hetland, M.L., Saetrom, P.: Temporal rule discovery using genetic programming and specialized hardware. In: Proc. of the 4th Int. Conf. on Recent Advances in Soft Computing (2002)
Interagon, A.S.: Digital processing device (April 2000), PCT/NO99/00308
Interagon, A.S.: The Interagon query language : a reference guide (September 2002), http://www.interagon.com/pub/whitepapers/IQL.reference-latest.pdf
Keogh, E., Folias, T.: The UCR time series data mining archive (September 2002), http://www.cs.ucr.edu/~eamonn/TSDMA
Keogh, E., Lonardi, S., Chiu, W.: Finding surprising patterns in a time series database in linear time and space. In: Proc. of the 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 550–556 (2002)
Federal Reserve Statistical Release. Foreign exchange rates 1971–2002 (October 2002), http://www.federalreserve.gov/Releases/H10/Hist
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hetland, M.L., Sætrom, P. (2003). The Role of Discretization Parameters in Sequence Rule Evolution. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_71
Download citation
DOI: https://doi.org/10.1007/978-3-540-45224-9_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40803-1
Online ISBN: 978-3-540-45224-9
eBook Packages: Springer Book Archive