Abstract
ICAS is an incremental concept acquisition system using attribute-based description. It includes an algorithm for leaming concept, which induces a rule set from an example set based on the probability theory, and an algorithm for refining the rule set. This paper also introduces the learning cycles, a very useful idea of ICAS. In fact, concept acquisition by ICAS is an incremental process consisting of many such learning cycles. Also the design and implementation of ICAS are given.
This is a preview of subscription content, log in via an institution to check access.
References
J. Hong, AE1: An Extension Matrix Approximate Method for the General Covering Problem.,Intl. J. of Computer and Information Science, 1985, 14 (6).
Quinlan, J. R., Learning Efficient Classification Procedures and Their Application, to Chess and Games. In “Machine Learning: An Artificial Intelligence Approach”, R. S. Michalski, J. G. Carbonell and T. M. Mitchell, editors, Palo Alto, CA: Tioga Press, 1983, 463–482.
Mitchalski, R. S., A Theory and Methodology of Inductive Learning. In “Machine Learning: An Artificial Intelligence Approach”, R. S. Michalski, J. G. Carbonell and T. M. Mitchell, editors, Palo Alto, CA: Tioga Press, 1983, 83–124.
Utgoff, P. E., ID5: An Incremental ID3. Proceedings of the AAAI Symposium, Computer Game Playing, 1986, 107–120.
Cendrowska, J., PRISM: Algorithm for Inducing Modular Rules.International Journal of Man-Machine Studies, 1988, 349–370.
Author information
Authors and Affiliations
Additional information
The project is supported by the National Natural Science Foundation of China.
Rights and permissions
About this article
Cite this article
Chen, S., Chen, B. & Pan, J. ICAS: An incremental concept acquisition system using attribute-based description. J. of Comput. Sci. & Technol. 7, 284–288 (1992). https://doi.org/10.1007/BF02946579
Received:
Revised:
Issue Date:
DOI: https://doi.org/10.1007/BF02946579