Abstract
A two-stage approach for inducing rules from examples is presented. The first stage consists in a breadth-first exploration which generates all ‘relevant’ rules. The second stage consists in selecting a subset of these rules so as to produce a ‘satisfactory’ description. Rough sets concepts may be used in cases of incomplete or inconsistent examples.
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
J.E. Beasley. An algorithm for set covering problem. European Journal of Operational Research, 31: 85–33, 1987.
J.E. Beasley. A lagrangian heuristic for set-covering problems. Naval Research Logistics, 37: 151–164, 1990.
J. Cendrowska. PRISM: an algorithm for inducing modular rules. Int. J. Man-Machine Studies, 27: 349–370, 1987.
N. Christofides and S. Korman. A computational survey of methods for the set covering problem. Management Science, 21 (5): 591–599, 1975.
N. Christofides and J. Paixâo. Algorithms for large set covering problems. Annals of Operations Research, 43: 261–277, 1993.
V. Chvatal. A greedy heuristic for the set-covering problem. Mathematics of Operations Research, 24 (3): 233–235, 1979.
P. Clark and T. Niblett. The CN2 induction algorithm. Machine Learning, 3: 261–283, 1989.
J.W. Grzymala-Busse. On the unknown attribute values in learning from examples. In Proc. of the ISMIS’91 6th Int. Symp. on Methodologies for Intelligent Systems, pages 368–377, 1991.
J.W. Grzymala-Busse. LERS-a system for learning from examples based on rough sets. In R. Slowinski, editor, Intelligent Decision Support, pages 3–18. Kluwer Academic Publishers, 1992.
D.S. Hochbaum. Approximation algorithm for the set covering and vertex cover problems. SIAM J. Comput., 11: 555–556, 1982.
Y. Kodratoff, M. Manago, and J. Blythe. Generalization and noise. Int. J. Man-Machine Studies, 27: 181–204, 1987.
R.S. Michalski. A theory and methodology of inductive learning. In R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach, pages 83–134. Morgan Kaufman, 1983.
R.S. Michalski and R.L. Chilausky. Learning by being told and learning from examples: an experimental comparison of the two methods of knowledge acquisition in the context of developing an expert system for soybean disease diagnosis. Policy Analysis and Information Systems Journal, 4 (2): 125–160, 1980.
R.S. Michalski, I. Mozetic, J. Hong, and N. Lavrac. The multipurpose incremental learning system AQ15 and its testing application to three medical domains. In Proc. of the 5th Nat. Conf. on Artificial Intelligence AAAI-86, Philadelphia, pages 1041–1045, 1986.
Z. Pawlak. Rough sets. Int. J. Computer and Information Sci., 11: 341–356, 1982.
Z. Pawlak. Rough Sets - Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, 1991.
J.R. Quinlan. Discovering rules from large collections of examples - a case study. In D. Michie, editor, Experts Systems in the Microelectronics Age. Edinburgh University Press, 1979.
J.R. Quinlan. The effect of noise on concept learning. In R.S. Michalski, J.G. Carbonell, and T.M. Mitchell, editors, Machine Learning: An Artificial Intelligence Approach Vol.II, pages 83–134. Morgan Kaufman, 1986.
J.R. Quinlan. Unknown attribute values in induction. In Proc. of the 6th Int. Workshop on Machine Learning, pages 164–168, 1989.
A. Skowron. Boolean reasoning for decision rules generation. In J. Komorowski and Z.W. Ras, editors, Methodologies for Intelligent Systems, LN in AI 689, pages 295–305. Springer Verlag, Berlin, 1993.
R. Slowinski, editor. Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory. Kluwer Academic Publishers, 1992.
S.K.M. Wong and W. Ziarko. On learning and evaluation of decision rules in the context of rough sets. In Proc. of the ACM SIGART Int. Symp. on Methodologies for Intelligent Systems, pages 308–324, 1986.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1994 British Computer Society
About this paper
Cite this paper
Stefanowski, J., Vanderpooten, D. (1994). A General Two-Stage Approach to Inducing Rules from Examples. In: Ziarko, W.P. (eds) Rough Sets, Fuzzy Sets and Knowledge Discovery. Workshops in Computing. Springer, London. https://doi.org/10.1007/978-1-4471-3238-7_37
Download citation
DOI: https://doi.org/10.1007/978-1-4471-3238-7_37
Publisher Name: Springer, London
Print ISBN: 978-3-540-19885-7
Online ISBN: 978-1-4471-3238-7
eBook Packages: Springer Book Archive