Abstract
This paper describes the LEI algorithm for empirical induction. The LEI algorithm provides efficient empirical induction for discrete attribute value data. It derives a classification procedure in the form of a set of predicate logic classification rules. This contrasts with the only other efficient approach to exhaustive empirical induction, the derivatives of the CLS algorithm, which present their classification procedures in the form of a decision tree. The LEI algorithm will always find the simplest non-disjunctive rule that correctly classifies all examples of a single class where such a rule exists.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Buchanan, B. G. and Feigenbaum, E. A. “DENDRAL and Meta-DENDRAL: Their Applications Dimension”, Artificial Intellignece, Vol 11 (1978), pp. 5–24.
Dietterich, T. G. and Michalski, R. S. “Inductive Learning of Structural Descriptions: Evaluation Criteria and Comparitive Review of Selected Methods”, Artificial Intelligence, Vol 16 (1981), pp. 257–294.
Hunt, E. B., Marin, J. and Stone, P. J. (1966). Experiments in Induction. Academic Press, New York.
Michalski, R. S. (1977). “Synthesis of Optimal and Quasi-optimal Variable-valued Logic Formulas”, in Proceedings of the 1975 International Symposium on Multiple Valued Logic, Bloomington, Ind., pp. 76–87.
Michalski, R. S. (1984). “A Theory and Methodology of Inductive Learning”, in R. S. Michalski, J. G. Carbonell, and T. M. Mitchell (Eds.), Machine Learning: An Artificial Intelligence Approach, Springer-Verlag, Berlin, pp. 83–129.
Michalski, R. S. “Pattern recognition as rule-guided inductive inference”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol PAMI-2, No 4 (July 1980), pp 349–361.
Mitchell, T. M. (1977). “Version spaces: A candidate elimination approach to rule learning”, in Proceedings of the Fifth International Joint Conference on Artificial Intelligence, pp. 305–310.
Quinlan, J. R. “Induction of Decision Trees”, Machine Learning, Vol 1 (1986), pp. 81–106.
Quinlan, J. R. (1987a). “Generating Production Rules From Decision Trees”, in Proceedings of the Tenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, Los Altos, pp. 304–307.
Quinlan, J. R. (1987b). “Induction, Knowledge and Expert Systems”, in Proceedings of the Australian Joint Artificial Intelligence Conference., Sydney, pp. 223–240.
Winston, P. H. (1975). “Learning Structural Descriptions from Examples. In P. H. Winston (Ed.), The Psychology of Computer Vision, McGraw-Hill, New York, pp. 157–209.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1990 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Webb, G.I. (1990). Techniques for efficient empirical induction. In: Barter, C.J., Brooks, M.J. (eds) AI '88. AI 1988. Lecture Notes in Computer Science, vol 406. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-52062-7_82
Download citation
DOI: https://doi.org/10.1007/3-540-52062-7_82
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-52062-7
Online ISBN: 978-3-540-46875-2
eBook Packages: Springer Book Archive