Abstract
In this paper we present results of an experimental comparison (in terms of an error rate) of rule sets induced by the LERS data mining system with rule sets induced using the probabilistic rough classification (PRC). As follows from our experiments, the performance of LERS (possible rules) is significantly better than the best rule sets induced by PRC with any threshold (two-tailed test, 5% significance level). Additionally, the LERS possible rule approach to rule induction is significantly better than the LERS certain rule approach (two-tailed test, 5% significance level).
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Pawlak, Z.: Rough classification. International Journal of Human-Computer Studies 51, 369–383 (1999)
Grzymala-Busse, J.W.: Knowledge acquisition under uncertainty—A rough set approach. Journal of Intelligent & Robotic Systems 1, 3–16 (1988)
Grzymala-Busse, J.W.: Managing uncertainty in machine learning from examples. In: Proceedings of the Third Intelligent Information Systems Workshop, pp. 70–84 (1994)
Grzymala-Busse, J.W.: A new version of the rule induction system LERS. Fundamenta Informaticae 31, 27–39 (1997)
Stefanowski, J.: Algorithms of Decision Rule Induction in Data Mining. Poznan University of Technology Press, Poznan (2001)
Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Dordrecht (1991)
Yao, Y.Y.: Probabilistic rough set approximations. International Journal of Approximation Reasonong 49, 255–271 (2008)
Ziarko, W.: Probabilistic approach to rough sets. International Journal of Approximate Reasoning 49, 272–284 (2008)
Grzymala-Busse, J.W., Ziarko, W.: Data mining based on rough sets. In: Wang, J. (ed.) Data Mining: Opportunities and Challenges, pp. 142–173. Idea Group Publ., Hershey (2003)
Yao, Y.Y.: Interpreting concept learning in cognitive informatics and granular computing. IEEE Transactions on System, Man and Cybernetics B 39, 855–866 (2009)
Mitchell, T.M.: Generalization as search. Artificial Intelligence 18, 203–226 (1982)
Yao, Y.Y., Wong, S.K.M.: A decision theoretic framework for approximate concepts. International Journal of Man-Machine Studies 37, 103–119 (1996)
Ziarko, W.: Variable precision rough set model. Journal of Computer and System Sciences 46(1), 39–59 (1993)
Grzymala-Busse, J.W.: MLEM2: A new algorithm for rule induction from imperfect data. In: Proceedings of the 9th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp. 243–250 (2002)
Grzymala-Busse, J.W., Yao, Y.: A comparison of the LERS classification system and rule management in PRSM. In: Chan, C.-C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) RSCTC 2008. LNCS (LNAI), vol. 5306, pp. 202–210. Springer, Heidelberg (2008)
Grzymala-Busse, J.W.: LERS—a system for learning from examples based on rough sets. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)
Chan, C.C., Grzymala-Busse, J.W.: On the attribute redundancy and the learning programs ID3, PRISM, and LEM2. Technical report, Department of Computer Science, University of Kansas (1991)
Booker, L.B., Goldberg, D.E., Holland, J.F.: Classifier systems and genetic algorithms. In: Carbonell, J.G. (ed.) Machine Learning. Paradigms and Methods, pp. 235–282. MIT Press, Boston (1990)
Holland, J.H., Holyoak, K.J., Nisbett, R.E.: Induction. Processes of Inference, Learning, and Discovery. MIT Press, Boston (1986)
Chmielewski, M.R., Grzymala-Busse, J.W.: Global discretization of continuous attributes as preprocessing for machine learning. International Journal of Approximate Reasoning 15(4), 319–331 (1996)
Chao, L.L.: Introduction to Statistics. Brooks Cole Publishing Co., Monterey (1980)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Grzymala-Busse, J.W., Marepally, S.R., Yao, Y. (2010). An Empirical Comparison of Rule Sets Induced by LERS and Probabilistic Rough Classification. In: Szczuka, M., Kryszkiewicz, M., Ramanna, S., Jensen, R., Hu, Q. (eds) Rough Sets and Current Trends in Computing. RSCTC 2010. Lecture Notes in Computer Science(), vol 6086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13529-3_63
Download citation
DOI: https://doi.org/10.1007/978-3-642-13529-3_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13528-6
Online ISBN: 978-3-642-13529-3
eBook Packages: Computer ScienceComputer Science (R0)