Categorization and Construction of Rule Based Systems

  • Han Liu
  • Alexander Gegov
  • Frederic Stahl
Part of the Communications in Computer and Information Science book series (CCIS, volume 459)


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.


Data Mining Machine Learning Rule Based Systems Rule Based Classification if-then Rules 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Partridge, D., Hussain, K.M.: Knowledge Based Information Systems. Mc-Graw Hill (1994)Google Scholar
  2. 2.
    Gegov, A.: Fuzzy Networks for Complex Systems: A Modular Rule Base Approach. Springer, Berlin (2010)Google Scholar
  3. 3.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman (1993)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    Bramer, M.A.: Principles of Data Mining. Springer, London (2007)zbMATHGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    Cendrowska, J.: PRISM: An Algorithm for Inducing Modular Rules. International Journal of Man-Machine Studies 27, 349–370 (1987)CrossRefzbMATHGoogle Scholar
  8. 8.
    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)Google Scholar
  9. 9.
    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)CrossRefGoogle Scholar
  10. 10.
    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)Google Scholar
  11. 11.
    Bramer, M.A.: Inducer: a Public Domain Workbench for Data Mining. International Journal of Systems Science 36(14), 909–919 (2005)CrossRefzbMATHGoogle Scholar
  12. 12.
    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)CrossRefGoogle Scholar
  13. 13.
    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)Google Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Shannon, C.: A Mathematical Theory of Communication. Bell System Technical Journal 27(3), 379–423 (1948)CrossRefzbMATHMathSciNetGoogle Scholar
  16. 16.
    Deng, X.: A Covering-based Algorithm for Classification: PRISM. CS831: Knowledge Discover in Databases (2012)Google Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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)Google Scholar
  19. 19.
    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)Google Scholar
  20. 20.
    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)Google Scholar
  21. 21.
    Aksoy, M.S.: A Review of Rules Families of Algorithms. Mathematical and Computational Applications 13(1), 51–60 (2008)zbMATHMathSciNetGoogle Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    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)Google Scholar
  24. 24.
    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)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Han Liu
    • 1
  • Alexander Gegov
    • 1
  • Frederic Stahl
    • 2
  1. 1.School of ComputingUniversity of PortsmouthPortsmouthUnited Kingdom
  2. 2.School of Systems EngineeringUniversity of ReadingReadingUnited Kingdom

Personalised recommendations