Abstract
Rough Set theory and Granular Computing (GrC) have a great impact on the study of intelligent information systems. This paper investigates the feasibility of applying Rough Set theory and Granular Computing (GrC) to deal with imperfect data in Inductive Logic Programming (ILP). We propose a hybrid approach, RS-ILP, to deal with some kinds of imperfect data which occur in real-world applications.
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
S. Dzeroski, “Inductive Logic Programming and Knowledge Discovery in Databases”, Advances in KDD, MITPress, 117–151, 1996.
N. Lavrac, S. Dzeroski, and I. Bratko “Handling Imperfect Data in Inductive Logic Programming”, in L. de Raedt (Eds), Advances in Inductive Logic Programming, IOS Press, 48–64, 1996.
C. Liu, N. Zhong, and S. Ohsuga, “Constraint ILP and its Application to KDD”, Proc. of IJCAI-97 Workshop on Frontiers of ILP, Nagoya, Japan, 103–104, 1997.
C. Liu and N. Zhong, “Rough Problem Settings for Inductive Logic Programming”, Zhong, N., Skowron, A., and Ohsuga, S. (eds.) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing, Springer LNAI 1711, 168–177, 1999.
T.M. Mitchell, Machine Learning, McGraw-Hill, 1997.
S. Moyle and S. Muggleton, “Learning Programs in the Event Calculus”, in Proc. 7th International Workshop on ILP, 205–212, 1997.
S. Muggleton, “Inductive Logic Programming”, New Generation Computing, 8(4):295–317, 1991.
S. Muggleton (Eds), Inductive Logic Programming, Academic Press, 1992
Z. Pawlak, “Rough Sets”, International Journal of Computer and Information Science, Vol.11, 341–356, 1982.
Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Boston, 1991.
Z. Pawlak, “Granularity of knowledge, indiscernibility and rough sets”, Proc. 1998 IEEE International Conference on Fuzzy Systems, 106–110, 1998.
Y.Y. Yao and N. Zhong, “An Analysis of Quantitative Measures Associated with Rules”, N. Zhong and L. Zhou (Eds), Methodologies for Knowledge Discovery and Data Mining, Springer LNAI 1574, 479–488, 1999.
Y.Y. Yao, “Granular Computing using Neighborhood Systems”, Roy, R., Furuhashi, T., and Chawdhry, P.K. (eds.) Advances in Soft Computing: Engineering Design and Manufacturing, Springer 539–553, 1999
Y.Y. Yao, Granular Computing: Basic Issues and Possible Solutions, Proc. JCIS 2000, invited session on Granular Computing and Data Mining, Vol.1, 186–189, 2000.
L.A. Zadeh, “Fuzzy Sets and Information Granularity”, Gupta, N., Ragade, R. and Yager, R. (Eds.) Advances in Fuzzy Set Theory and Applications, North-Holland, Amsterdam, 3–18, 1979.
L.A. Zadeh, “Toward a Theory of Fuzzy Information Granulation and Its Centrality in Human Reasoning and Fuzzy Logic”, Fuzzy Sets and Systems, Vol.19, 111–127, 1997.
N. Zhong, J. Dong, and S. Ohsuga, “Data Mining: A Probabilistic Rough Set Approach”, L. Polkowski and A. Skowron (Eds) Rough Sets in Knowledge Discovery, Physica-Verlag, 127–146, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, C., Zhong, N. (2001). Dealing with Imperfect Data by RS-ILP. In: Terano, T., Ohsawa, Y., Nishida, T., Namatame, A., Tsumoto, S., Washio, T. (eds) New Frontiers in Artificial Intelligence. JSAI 2001. Lecture Notes in Computer Science(), vol 2253. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45548-5_45
Download citation
DOI: https://doi.org/10.1007/3-540-45548-5_45
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43070-4
Online ISBN: 978-3-540-45548-6
eBook Packages: Springer Book Archive