Abstract
The Prism family is an alternative set of predictive data mining algorithms to the more established decision tree data mining algorithms. Prism classifiers are more expressive and user friendly compared with decision trees and achieve a similar accuracy compared with that of decision trees and even outperform decision trees in some cases. This is especially the case where there is noise and clashes in the training data. However, Prism algorithms still tend to overfit on noisy data; this has led to the development of pruning methods which have allowed the Prism algorithms to generalise better over the dataset. The work presented in this paper aims to address the problem of overfitting at rule induction stage for numerical attributes by proposing a new numerical rule term structure based on the Gauss Probability Density Distribution. This new rule term structure is not only expected to lead to a more robust classifier, but also lowers the computational requirements as it needs to induce fewer rule terms.
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
Similar content being viewed by others
References
Bramer, M.: Principles of Data Mining. Undergraduate Topics in Computer Science. Springer International Publishing (2013)
Bramer, M.A.: An information-theoretic approach to the pre-pruning of classification rules. In: Neumann, B., Musen, M., Studer, R. (eds) Intelligent Information Processing, pp. 201–212. Kluwer (2002)
Cendrowska, J.: PRISM: an algorithm for inducing modular rules (1987)
Le, T., Stahl, F., Gomes, J., Gaber, M.M. Di Fatta, G.: Computationally efficient rule-based classification for continuous streaming data. In: Research and Development in Intelligent Systems XXXI, pp. 21–34. Springer (2014)
Lichman, M.: UCI machine learning repository (2013)
Ross, J.: Quinlan induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Stahl, F., Bramer, M.: Computationally efficient induction of classification rules with the PMCRI and j-pmcri frameworks. Knowl.-Based Syst. 35, 49–63 (2012)
Stahl, F., Bramer, M.: Jmax-pruning: a facility for the information theoretic pruning of modular classification rules. Knowl.-Based Syst. 29, 12–19 (2012)
Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques: Practical Machine Learning Tools and Techniques. Elsevier Science, The Morgan Kaufmann Series in Data Management Systems (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Almutairi, M., Stahl, F., Jennings, M., Le, T., Bramer, M. (2016). Towards Expressive Modular Rule Induction for Numerical Attributes. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXXIII. SGAI 2016. Springer, Cham. https://doi.org/10.1007/978-3-319-47175-4_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-47175-4_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-47174-7
Online ISBN: 978-3-319-47175-4
eBook Packages: Computer ScienceComputer Science (R0)