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Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions pp 1–14Cite as

Introduction

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 156))

Abstract

In this chapter, we begin with the discussion of decision and inhibitory interpretations of decision tables with many-valued decisions, then consider results obtained in the frameworks of the three main directions of the study, describe the contents of the book, and finally add some words about its use.

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References

  1. AbouEisha, H., Amin, T., Chikalov, I., Hussain, S., Moshkov, M.: Extensions of Dynamic Programming for Combinatorial Optimization and Data Mining, Intelligent Systems Reference Library, vol. 146. Springer, Berlin (2019)

    Book  Google Scholar 

  2. 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 

  3. Alkhalid, A., Amin, T., Chikalov, I., Hussain, S., Moshkov, M., Zielosko, B.: Optimization and analysis of decision trees and rules: dynamic programming approach. Int. J. Gen. Syst. 42(6), 614–634 (2013)

    Article  MathSciNet  Google Scholar 

  4. Alsolami, F., Chikalov, I., Moshkov, M., Zielosko, B.: Length and coverage of inhibitory decision rules. In: Nguyen, N.T., Hoang, K., Jedrzejowicz, P. (eds.) Computational Collective Intelligence. Technologies and Applications – 4th International Conference, ICCCI 2012, Ho Chi Minh City, Vietnam, 28–30 Nov 2012, Part II. Lecture Notes in Computer Science, vol. 7654, pp. 325–334. Springer (2012)

    Google Scholar 

  5. Alsolami, F., Chikalov, I., Moshkov, M., Zielosko, B.: Optimization of inhibitory decision rules relative to length and coverage. In: Li, T., Nguyen, H.S., Wang, G., Grzymala-Busse, J.W., Janicki, R., Hassanien, A.E., Yu, H. (eds.) Rough Sets and Knowledge Technology – 7th International Conference, RSKT 2012, Chengdu, China, 17–20 Aug 2012. Lecture Notes in Computer Science, vol. 7414, pp. 149–154. Springer (2012)

    Google Scholar 

  6. Alsolami, F., Chikalov, I., Moshkov, M.: Sequential optimization of approximate inhibitory rules relative to the length, coverage and number of misclassifications. In: Lingras, P., Wolski, M., Cornelis, C., Mitra, S., Wasilewski, P. (eds.) Rough Sets and Knowledge Technology – 8th International Conference, RSKT 2013, Halifax, NS, Canada, 11–14 Oct 2013. Lecture Notes in Computer Science, vol. 8171, pp. 154–165. Springer, Berlin (2013)

    Google Scholar 

  7. Alsolami, F., Chikalov, I., Moshkov, M., Zielosko, B.: Optimization of approximate inhibitory rules relative to number of misclassifications. In: Watada, J., Jain, L.C., Howlett, R.J., Mukai, N., Asakura, K. (eds.) 17th International Conference in Knowledge Based and Intelligent Information and Engineering Systems, KES 2013, Kitakyushu, Japan, 9–11 Sept 2013. Procedia Computer Science, vol. 22, pp. 295–302. Elsevier (2013)

    Google Scholar 

  8. Azad, M., Moshkov, M.: Multi-stage optimization of decision and inhibitory trees for decision tables with many-valued decisions. Eur. J. Oper. Res. 263(3), 910–921 (2017)

    Article  MathSciNet  Google Scholar 

  9. Bayes, A.J.: A dynamic programming algorithm to optimise decision table code. Aust. Comput. J. 5(2), 77–79 (1973)

    Google Scholar 

  10. Blockeel, H., Schietgat, L., Struyf, J., Dzeroski, S., Clare, A.: Decision trees for hierarchical multilabel classification: A case study in functional genomics. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) Knowledge Discovery in Databases: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006, Berlin, Germany, 18–22 Sept 2006. Lecture Notes in Computer Science, vol. 4213, pp. 18–29. Springer (2006)

    Google Scholar 

  11. Boros, E., Hammer, P.L., Ibaraki, T., Kogan, A.: Logical analysis of numerical data. Math. Program. 79, 163–190 (1997)

    Google Scholar 

  12. Boros, E., Hammer, P.L., Ibaraki, T., Kogan, A., Mayoraz, E., Muchnik, I.: An implementation of logical analysis of data. IEEE Trans. Knowl. Data Eng. 12, 292–306 (2000)

    Google Scholar 

  13. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognit. 37(9), 1757–1771 (2004)

    Google Scholar 

  14. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J.: Classification and Regression Trees. Wadsworth and Brooks, Monterey, CA (1984)

    MATH  Google Scholar 

  15. 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 

  16. Chegis, I.A., Yablonskii, S.V.: Logical methods of control of work of electric schemes. Trudy Mat. Inst. Steklov (in Russian) 51, 270–360 (1958)

    Google Scholar 

  17. Chikalov, I., Hussain, S., Moshkov, M.: Relationships between depth and number of misclassifications for decision trees. In: Kuznetsov, S.O., Slezak, D., Hepting, D.H., Mirkin, B.G. (eds.) 13th International Conference Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2011, Moscow, Russia, 25–27 June 2011. Lecture Notes in Computer Science, vol. 6743, pp. 286–292. Springer (2011)

    Google Scholar 

  18. Chikalov, I., Hussain, S., Moshkov, M.: Relationships between number of nodes and number of misclassifications for decision trees. In: Yao, J., Yang, Y., Slowinski, R., Greco, S., Li, H., Mitra, S., Polkowski, L. (eds.) Rough Sets and Current Trends in Computing – 8th International Conference, RSCTC 2012, Chengdu, China, 17–20 Aug 2012. Lecture Notes in Computer Science, vol. 7413, pp. 212–218. Springer (2012)

    Google Scholar 

  19. Crama, Y., Hammer, P.L., Ibaraki, T.: Cause-effect relationships and partially defined Boolean functions. Ann. Oper. Res. 16, 299–326 (1988)

    Google Scholar 

  20. Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Two families of classification algorithms. In: An, A., Stefanowski, J., Ramanna, S., Butz, C.J., Pedrycz, W., Wang, G. (eds.) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, 11th International Conference, RSFDGrC 2007, Toronto, Canada, 14–16 May 2007. Lecture Notes in Computer Science, vol. 4482, pp. 297–304. Springer (2007)

    Google Scholar 

  21. Delimata, P., Moshkov, M., Skowron, A., Suraj, Z.: Inhibitory Rules in Data Analysis: A Rough Set Approach. Studies in Computational Intelligence, vol. 163. Springer, Berlin (2009)

    MATH  Google Scholar 

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

    Google Scholar 

  23. Garey, M.R.: Optimal binary identification procedures. SIAM J. Appl. Math. 23, 173–186 (1972)

    Google Scholar 

  24. Hussain, S.: Relationships among various parameters for decision tree optimization. In: Faucher, C., Jain, L.C. (eds.) Innovations in Intelligent Machines-4 – Recent Advances in Knowledge Engineering. Studies in Computational Intelligence, vol. 514, pp. 393–410. Springer, Berlin (2014)

    Google Scholar 

  25. Lichman, M.: UCI Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences (2013). http://archive.ics.uci.edu/ml

  26. Martelli, A., Montanari, U.: Optimizing decision trees through heuristically guided search. Commun. ACM 21(12), 1025–1039 (1978)

    Google Scholar 

  27. Moshkov, M.: On the uniqueness of dead-end tests for recognition problems with linear decision rules. In: Markov, A.A. (ed.) Combinatorial-Algebraic Methods in Applied Mathematics (in Russian), pp. 97–109. Gorky University Press, Gorky (1981)

    Google Scholar 

  28. Moshkov, M.: Decision Trees. Theory and Applications (in Russian). Nizhny Novgorod University Publishers, Nizhny Novgorod (1994)

    MATH  Google Scholar 

  29. Moshkov, M.: Lower bounds for the time complexity of deterministic conditional tests. Diskret. Mat. (in Russian) 8(3), 98–110 (1996). https://doi.org/10.4213/dm538

  30. Moshkov, M.: Unimprovable upper bounds on complexity of decision trees over information systems. Found. Comput. Decis. Sci. 21, 219–231 (1996)

    Google Scholar 

  31. Moshkov, M.: On time complexity of decision trees. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery 1: Methodology and Applications. Studies in Fuzziness and Soft Computing, vol. 18, pp. 160–191. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  32. Moshkov, M.: Elements of Mathematical Theory of Tests with Applications to Problems of Discrete Optimization. Nizhni Novgorod University Publishers, Nizhni Novgorod (2001). (in Russian)

    Google Scholar 

  33. Moshkov, M.: Classification of infinite information systems depending on complexity of decision trees and decision rule systems. Fundam. Inform. 54, 345–368 (2003)

    Google Scholar 

  34. Moshkov, M.: Time complexity of decision trees. In: Peters, J.F., Skowron, A. (eds.) Trans. Rough Sets III. Lecture Notes in Computer Science, vol. 3400, pp. 244–459. Springer (2005)

    Google Scholar 

  35. Moshkov, M.: Bounds on complexity and algorithms for construction of deterministic conditional tests. In: Mathematical Problems of Cybernetics (in Russian), vol. 16, pp. 79–124. Fizmatlit, Moscow (2007). http://library.keldysh.ru/mvk.asp?id=2007-79

  36. Moshkov, M., Chikalov, I.: On algorithm for constructing of decision trees with minimal depth. Fundam. Inform. 41(3), 295–299 (2000)

    Google Scholar 

  37. Moshkov, M., Chikalov, I.: Sequential optimization of decision trees relatively different complexity measures. In: 6th International Conference Soft Computing and Distributed Processing, Rzeszòw, Poland, 24–25 June, pp. 53–56 (2002)

    Google Scholar 

  38. Moshkov, M., Chikalov, I.: Consecutive optimization of decision trees concerning various complexity measures. Fundam. Inform. 61(2), 87–96 (2004)

    Google Scholar 

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

    Book  Google Scholar 

  40. Moshkov, M., Skowron, A., Suraj, Z.: Maximal consistent extensions of information systems relative to their theories. Inf. Sci. 178(12), 2600–2620 (2008)

    Google Scholar 

  41. Pawlak, Z.: Rough Sets - Theoretical Aspect of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  42. Pawlak, Z., Skowron, A.: Rudiments of rough sets. Inf. Sci. 177(1), 3–27 (2007)

    Google Scholar 

  43. Read, J., Martino, L., Olmos, P.M., Luengo, D.: Scalable multi-output label prediction: from classifier chains to classifier trellises. Pattern Recognit. 48(6), 2096–2109 (2015)

    Google Scholar 

  44. Rokach, L., Maimon, O.: Data Mining with Decision Trees: Theory and Applications. World Scientific Publishing, River Edge (2008)

    MATH  Google Scholar 

  45. Schumacher, H., Sevcik, K.C.: The synthetic approach to decision table conversion. Commun. ACM 19(6), 343–351 (1976)

    Google Scholar 

  46. Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Słowiński, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362. Kluwer Academic Publishers, Dordrecht (1992)

    Google Scholar 

  47. Skowron, A., Suraj, Z.: Rough sets and concurrency. Bull. Pol. Acad. Sci. 41(3), 237–254 (1993)

    Google Scholar 

  48. Soloviev, N.A.: Tests (Theory, Construction, Applications). Nauka, Novosibirsk (1978). (in Russian)

    Google Scholar 

  49. Suraj, Z.: Some remarks on extensions and restrictions of information systems. In: Ziarko, W., Yao, Y.Y. (eds.) Rough Sets and Current Trends in Computing, Second International Conference, RSCTC 2000, Banff, Canada, 16–19 Oct 2000, Revised Papers. Lecture Notes in Computer Science, vol. 2005, pp. 204–211. Springer (2001)

    Google Scholar 

  50. Wieczorkowska, A., Synak, P., Lewis, R.A., Ras, Z.W.: Extracting emotions from music data. In:  Hacid, M., Murray, N.V., Ras, Z.W., Tsumoto, S. (eds.) Foundations of Intelligent Systems, 15th International Symposium, ISMIS 2005, Saratoga Springs, NY, USA, 25–28 May 2005. Lecture Notes in Computer Science, vol. 3488, pp. 456–465. Springer (2005)

    Google Scholar 

  51. Zhou, Z.H., Jiang, K., Li, M.: Multi-instance learning based web mining. Appl. Intell. 22(2), 135–147 (2005)

    Google Scholar 

  52. Zhuravlev, J.I.: On a class of partial Boolean functions. Diskret. Analiz (in Russian) 2, 23–27 (1964)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Fawaz Alsolami & Mikhail Moshkov

  2. Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Mohammad Azad & Igor Chikalov

Authors
  1. Fawaz Alsolami
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  2. Mohammad Azad
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  3. Igor Chikalov
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  4. Mikhail Moshkov
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Correspondence to Fawaz Alsolami .

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Alsolami, F., Azad, M., Chikalov, I., Moshkov, M. (2020). Introduction. In: Decision and Inhibitory Trees and Rules for Decision Tables with Many-valued Decisions. Intelligent Systems Reference Library, vol 156. Springer, Cham. https://doi.org/10.1007/978-3-030-12854-8_1

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