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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 146))

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Abstract

Decision rules are mappings from a set of conditions to an outcome. There are many uses for decision rules and even more methods for construction of decision rules. Decision rules are most commonly used in sets. Such sets can be referred to as systems of decision rules. Both decision rules and systems of decision rules can be analyzed with regards to different criteria (cost functions). In this chapter, we provide definitions and basic concepts with regards to decision rules and systems of decision rules.

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References

  1. Alkhalid, A., Amin, T., Chikalov, I., Hussain, S., Moshkov, M., Zielosko, B.: Dagger: A tool for analysis and optimization of decision trees and rules. In: Ficarra, F.V.C., Kratky, A., Veltman, K.H., Ficarra, M.C., Nicol, E., Brie, M. (eds.) Computational Informatics, Social Factors and New Information Technologies: Hypermedia Perspectives and Avant-Garde Experiencies in the Era of Communicability Expansion, pp. 29–39. Blue Herons (2011)

    Google Scholar 

  2. Amin, T., Chikalov, I., Moshkov, M., Zielosko, B.: Dynamic programming approach for exact decision rule optimization. In: Skowron, A., Suraj, Z. (eds.) Rough Sets and Intelligent Systems – Professor Zdzisław Pawlak in Memoriam – Volume 1. Intelligent Systems Reference Library, vol. 42, pp. 211–228. Springer, Berlin (2013)

    Chapter  Google Scholar 

  3. Bazan, J.G., Szczuka, M.S.: RSES and RSESlib - a collection of tools for rough set computations. In: Ziarko, W., Yao, Y.Y. (eds.) Rough Sets and Current Trends in Computing, Second International Conference, RSCTC 2000, Banff, Canada, October 16–19, 2000, Revised Papers. Lecture Notes in Computer Science, vol. 2005, pp. 106–113. Springer, Berlin (2001)

    Chapter  Google Scholar 

  4. Bazan, J.G., Szczuka, M.S., Wojna, A., Wojnarski, M.: On the evolution of rough set exploration system. In: Tsumoto, S., Słowinski, R., Komorowski, H.J., Grzymała-Busse, J.W. (eds.) Rough Sets and Current Trends in Computing - 4th International Conference, RSCTC 2004, Uppsala, Sweden, June 1–5, 2004. Lecture Notes in Computer Science, vol. 3066, pp. 592–601. Springer, Berlin (2004)

    Chapter  Google Scholar 

  5. Carbonell, J.G., Michalski, R.S., Mitchell, T.M.: An overview of machine learning. In: Michalski, R.S., Carbonell, J.G., Mitchell, T.M. (eds.) Machine Learning, An Artificial Intelligence Approach, pp. 1–23. Tioga Publishing, Palo Alto (1983)

    Google Scholar 

  6. Clark, P., Niblett, T.: The CN2 induction algoirthm. Mach. Learn. 3(4), 261–283 (1989)

    Google Scholar 

  7. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  8. Dasarathy, B.V. (ed.): Nearest Neighbor(NN) Norms: NN Pattern Classification Techniques. IEEE Computer Society Press, Los Alamitos (1990)

    Google Scholar 

  9. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37–54 (1996)

    Google Scholar 

  10. Fürnkranz, J.: Separate-and-conquer rule learning. Artif. Intell. Rev. 13(1), 3–54 (1999)

    Article  Google Scholar 

  11. Góra, G., Wojna, A.: RIONA: A new classification system combining rule induction and instance-based learning. Fundam. Inform. 51(4), 369–390 (2002)

    Google Scholar 

  12. Grzymała-Busse, J.W.: LERS – a system for learning from examples based on rough sets. In: Słowiński, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Sets Theory, pp. 3–18. Kluwer Academic Publishers, Dordrecht (1992)

    Chapter  Google Scholar 

  13. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  14. Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach, Studies in Computational Intelligence, vol. 360. Springer, Heidelberg (2011)

    Book  Google Scholar 

  15. Moshkov, M., Piliszczuk, M., Zielosko, B.: Partial Covers, Reducts and Decision Rules in Rough Sets - Theory and Applications, Studies in Computational Intelligence, vol. 145. Springer, Heidelberg (2008)

    MATH  Google Scholar 

  16. Muggleton, S.: Learning stochastic logic programs. Electron. Trans. Artif. Intell. 4(B), 141–153 (2000)

    Google Scholar 

  17. Øhrn, A., Komorowski, J., Skowron, A., Synak, P.: The design and implementation of a knowledge discovery toolkit based on rough sets: The ROSETTA system. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Studies in Fuzziness and Soft Computing, vol. 18, pp. 376–399. Physica-Verlag (1998)

    Google Scholar 

  18. Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning. Inf. Sci. 177(1), 41–73 (2007)

    Article  MathSciNet  Google Scholar 

  19. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)

    Google Scholar 

  20. Quinlan, J.R.: Simplifying decision trees. Int. J. Man. Mach. Stud. 27(3), 221–234 (1987)

    Article  Google Scholar 

  21. Rumelhart, D.E., McClelland, J.L., CORPORATE PDP Research Group (ed.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations. MIT Press, Cambridge (1986)

    Google Scholar 

  22. Sikora, M.: Decision rule-based data models using TRS and NetTRS - methods and algorithms. In: Peters, J.F., Skowron, A. (eds.) Trans. Rough Sets XI. Lecture Notes in Computer Science, vol. 5946, pp. 130–160. Springer, Berlin (2010)

    Google Scholar 

  23. Ślȩzak, D., Wróblewski, J.: Order based genetic algorithms for the search of approximate entropy reducts. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing – 9th International Conference, RSFDGrC 2003, Chongqing, China, May 26–29, 2003. Lecture Notes in Computer Science, vol. 2639, pp. 308–311. Springer, Berlin (2003)

    Google Scholar 

  24. Zielosko, B., Chikalov, I., Moshkov, M., Amin, T.: Optimization of decision rules based on dynamic programming approach. In: Faucher, C., Jain, L.C. (eds.) Innovations in Intelligent Machines-4 – Recent Advances in Knowledge Engineering. Studies in Computational Intelligence, vol. 514, pp. 369–392. Springer, Berlin (2014)

    Google Scholar 

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Correspondence to Hassan AbouEisha .

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AbouEisha, H., Amin, T., Chikalov, I., Hussain, S., Moshkov, M. (2019). Different Kinds of Rules and Systems of Rules. In: Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining. Intelligent Systems Reference Library, vol 146. Springer, Cham. https://doi.org/10.1007/978-3-319-91839-6_9

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