Abstract
Rough set approaches provide useful tools to find minimal decision rules. The obtained minimal decision rules are used to classify unseen objects. On the other hand, the condition parts of the minimal decision rules are sometimes used to design new objects which will be classified into the target decision class. While we are interested in the goodness of the set of obtained minimal decision rules in the former case, we are interested in the goodness of an individual minimal decision rule in the latter case. In this paper, we propose robustness measure as a new type of evaluation index for decision rules. The measure evaluates to what extent the decision rule maintains the goodness of classification against the partially-matched data. Numerical experiments are conducted to examine the effectiveness of robustness measure.
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
Pawlak, Z.: Rough Sets. International Journal of Computer and Information Sciences 11(5), 341–356 (1982)
Mori, N., Tanaka, H., Inoue, K. (eds.): Rough Sets and Kansei. Kaibundo, Tokyo (2006) (in Japanese)
Geng, L., Hamilton, H.J.: Interestingness Measures for Data Mining: A Survey. ACM Computing Surveys 38(9), 1–32 (2006)
Greco, S., Słowiński, R., Szczech, I.: Properties of Rule Interestingness Measures and Alternative Approaches to Normalization of Measures. Information Sciences 216, 1–16 (2012)
Lenca, P., Meyer, P., Vaillant, B., Lallich, S.: On Selecting Interestingness Measures for Association Rules: User Oriented Description and Multiple Criteria Decision Aid. European Journal of Operation Research 184, 610–626 (2008)
Mcgarry, K.: A Survey of Interestingness Measure for Knowledge Discovery. The Knowledge Engineering Review, 1–24 (2005)
Lenca, P., Vaillant, B., Lallich, S.: On the Robustness of Association Rules. In: Proceedings of 2006 IEEE Conference on Cybernetics and Intelligent Systems, pp. 596–601 (2006)
Le Bras, Y., Meyer, P., Lenca, P., Lallich, S.: A Robustness Measure of Association Rules. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part II. LNCS, vol. 6322, pp. 227–242. Springer, Heidelberg (2010)
Grzymala-Busse, J.W.: MLEM2 - Discretization During Rule Induction. In: Proceedings of the IIPWM 2003, pp. 499–508 (2003)
UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ohki, M., Inuiguchi, M. (2013). Robustness Measure of Decision Rules. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds) Rough Sets and Knowledge Technology. RSKT 2013. Lecture Notes in Computer Science(), vol 8171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41299-8_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-41299-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-41298-1
Online ISBN: 978-3-642-41299-8
eBook Packages: Computer ScienceComputer Science (R0)