Abstract
As a special way of human brains in learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly based on original knowledge learned before and the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data set incrementally. Our simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms, and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides, the simulation results also show that our algorithm outperforms ID4 in many aspects.
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
Schlimmer, J. C., Fisher, D A, Case study of incremental concept induction. Proceedings of the Fifth National Conf. on Artificial Intelligence, Los Altos, 1986.
Lu, R. Y., Artificial Intelligence, Scientific Press, 2002.
Wang, G. Y., Shi, H. B., Deng, W., A parallel neural network architecture based on nara model and sieving method, Chinese Journal of Computer, 19(9), 1996.
Wang, G. Y., Nie, N., PMSN: A Parallel Multi-Sieving Neural Network Architecture. Journal of Computer Research and Development, 36(Suppl.), pp. 21–25, 1999.
Wang, G. Y., Shi, H. B., Parallel Neural Network Architectures and Their Applications. In: Proceedings of Int. Conf. on Neural Networks, Perth, Australia, pp. 1234–1239, 1995.
Liu, Z. T., An Incremental Arithmetic for the Smallest Reduction of Attributes, Chinese Journal of Electronics, 27(11), pp. 96–98, 1999.
Wang, Z. H., Liu, Z. T, General and Incremental Algorithms of Rule Extraction Based on Concept Lattice, Chinese Journal of Computer, 22(1), pp. 66–70, 1999.
Wang, G. Y., Rough Set Theory and Knowledge Acquisition, Xi’an: Xi’an Jiaotong University Press, 2001.
Su, J., Gao, J., Metainformatio Based Rough Set Incrementally Rule Extraction Algorithm, Pattern Recognition and Artificial Intelligence, 14(4), pp. 428–433, 2001.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zheng, Z., Wang, G., Wu, Y. (2003). A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_16
Download citation
DOI: https://doi.org/10.1007/3-540-39205-X_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
eBook Packages: Springer Book Archive