Abstract
Expert systems have been increasingly popular for commercial importance. A rule based system is a special type of an expert system, which consists of a set of ‘if-then’ rules and can be applied as a decision support system in many areas such as healthcare, transportation and security. Rule based systems can be constructed based on both expert knowledge and data. This paper aims to introduce the theory of rule based systems especially on categorization and construction of such systems from a conceptual point of view. This paper also introduces rule based systems for classification tasks in detail.
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.
Similar content being viewed by others
References
Partridge, D., Hussain, K.M.: Knowledge Based Information Systems. Mc-Graw Hill (1994)
Gegov, A.: Fuzzy Networks for Complex Systems: A Modular Rule Base Approach. Springer, Berlin (2010)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman (1993)
Michalski, R.S.: On the Quasi-Minimal solution of the general covering problem. In: Proceedings of the Fifth International Symposium on Information Processing, Bled, Yugoslavia, pp. 125–128 (1969)
Bramer, M.A.: Principles of Data Mining. Springer, London (2007)
Liu, H., Gegov, A., Stahl, F.: Unified Framework for Construction of Rule Based Classification Systems. In: Pedrycz, W., Chen, S.M. (eds.) Springer, Berlin (in press)
Cendrowska, J.: PRISM: An Algorithm for Inducing Modular Rules. International Journal of Man-Machine Studies 27, 349–370 (1987)
Bramer, M.A.: Automatic Induction of Classification Rules from Examples using N-Prism. Research and Development in Intelligent Systems, vol. XVI, pp. 99–121. Springer, Cambridge (2000)
Stahl, F., Bramer, M.A.: Jmax-pruning: A Facility for the Information Theoretic Pruning of Modular Classification Rules. Knowledge-Based Systems 29, 12–19 (2012)
Stahl, F., Bramer, M.A.: Induction of Modular Classification Rules: using Jmax-pruning. In: Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 14–16. Springer, Heidelberg (2011)
Bramer, M.A.: Inducer: a Public Domain Workbench for Data Mining. International Journal of Systems Science 36(14), 909–919 (2005)
Stahl, F., Bramer, M.A.: Computationally Efficient Induction of Classification Rules with the PMCRI and J-PMCRI Frameworks. Knowledge-Based Systems 35, 49–63 (2012)
Bramer, M.A.: An Information-theoretic Approach to the Pre-pruning of Classification Rules. In: Musen, B.N., Studer, R. (eds.) Intelligent Information Processing, pp. 201–212. Kluwer (2002)
Liu, H., Gegov, A.: Induction of Modular Classification Rules by Information Entropy Based Rule Generation. In: Sgurev, V., Yager, R., Kacprzyk, J. (eds.) Innovative Issues in Intelligent Systems. Springer (in press)
Shannon, C.: A Mathematical Theory of Communication. Bell System Technical Journal 27(3), 379–423 (1948)
Deng, X.: A Covering-based Algorithm for Classification: PRISM. CS831: Knowledge Discover in Databases (2012)
Bramer, M.A.: Using J-Pruning to Reduce Overfitting of Classification Rules in Noisy Domains. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds.) DEXA 2002. LNCS, vol. 2453, p. 433. Springer, Heidelberg (2002)
Smyth, P., Goodman, R.M.: Rule Induction Using Information Theory. In: Piatetsky-Shapiro, G., Frawley, W.J. (eds.) Knowledge Discovery in Databases, pp. 159–176. AAAI Press (1991)
Bramer, M.A.: Using J-Pruning to Reduce Overfitting in Classification Trees. In: Research and Development in Intelligent Systems XVIII, pp. 25–38. Springer (2002)
Liu, H., Gegov, A., Stahl, F.: J-measure Based Hybrid Pruning for Complexity Reduction in Classification Rules. WSEAS Transaction on Systems 12(9), 433–446 (2013)
Aksoy, M.S.: A Review of Rules Families of Algorithms. Mathematical and Computational Applications 13(1), 51–60 (2008)
Quinlan, J.R.: Induction, Knowledge and Expert Systems. In: Gero, J.S., Stanton, R. (eds.) Artificial Intelligence Developments and Applications, Amsterdam, North Holland, pp. 253–271 (1988)
Michalski, R.S., et al.: The Multi-purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains. In: Proc. National Conf. on AI, Philadelphia, PA, pp. 1041–1044 (August 1996)
Quinlan, J.R.: Inductive Knowledge Acquisition: a Case Study. In: Quinlan, J.R. (ed.) Applications of Expert Systems, Quinlan, J, pp. 157–173. Turing Institute Press (1987)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Liu, H., Gegov, A., Stahl, F. (2014). Categorization and Construction of Rule Based Systems. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-11071-4_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11070-7
Online ISBN: 978-3-319-11071-4
eBook Packages: Computer ScienceComputer Science (R0)