Skip to main content
Springer Nature Link
Log in

Simulating Functioning of Decision Trees for Tasks on Decision Rule Systems

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14839))

Included in the following conference series:

Abstract

DRSs (Decision Rule Systems) and DTs (Decision Trees) are well known as classification tools, knowledge representation methods, and algorithms. Their clarity and ease of interpretation in data analysis are widely recognized. The study of the relationship between DTs and DRSs is an important problem in computer science. There are established methods for converting DTs to DRSs. In this work, we explore the inverse transformation problem, which is challenging. Rather than constructing a full DT that answers the tasks on DRSs, our research provides a greedy algorithm that simulates the functioning of a DT for an input array of feature values.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Abdelhalim, A., Traoré, I., Nakkabi, Y.: Creating decision trees from rules using RBDT-1. Comput. Intell. 32(2), 216–239 (2016)

    Article  MathSciNet  Google Scholar 

  2. Abdelhalim, A., Traore, I., Sayed, B.: RBDT-1: a new rule-based decision tree generation technique. In: Governatori, G., Hall, J., Paschke, A. (eds.) RuleML 2009. LNCS, vol. 5858, pp. 108–121. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04985-9_12

    Chapter  Google Scholar 

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

    Book  Google Scholar 

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

    Book  Google Scholar 

  5. Blum, M., Impagliazzo, R.: Generic oracles and oracle classes (extended abstract). In: 28th Annual Symposium on Foundations of Computer Science, Los Angeles, California, USA, 27–29 October 1987, pp. 118–126. IEEE Computer Society (1987)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. Boros, E., Hammer, P.L., Ibaraki, T., Kogan, A., Mayoraz, E., Muchnik, I.B.: An implementation of logical analysis of data. IEEE Trans. Knowl. Data Eng. 12(2), 292–306 (2000)

    Article  Google Scholar 

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

    Google Scholar 

  9. Buhrman, H., de Wolf, R.: Complexity measures and decision tree complexity: a survey. Theor. Comput. Sci. 288(1), 21–43 (2002)

    Article  MathSciNet  Google Scholar 

  10. Cao, H.E.C., Sarlin, R., Jung, A.: Learning explainable decision rules via maximum satisfiability. IEEE Access 8, 218180–218185 (2020)

    Article  Google Scholar 

  11. Chikalov, I., Lozin, V.V., Lozina, I., Moshkov, M., Nguyen, H.S., Skowron, A., Zielosko, B.: Three Approaches to Data Analysis - Test Theory, Rough Sets and Logical Analysis of Data. Intelligent Systems Reference Library, vol. 41. Springer, Cham (2013). https://doi.org/10.1007/978-3-642-28667-4

    Book  Google Scholar 

  12. Durdymyradov, K., Moshkov, M.: Construction of decision trees and acyclic decision graphs from decision rule systems. arXiv:2305.01721 [cs.AI] (2023)

  13. Durdymyradov, K., Moshkov, M.: Bounds on depth of decision trees derived from decision rule systems with discrete attributes. Ann. Math. Artif. Intell. (2024). https://doi.org/10.1007/s10472-024-09933-x

  14. Durdymyradov, K., Moshkov, M.: Greedy algorithm for inference of decision trees from decision rule systems. arXiv:2401.06793 [cs.AI] (2024)

  15. Fürnkranz, J., Gamberger, D., Lavrac, N.: Foundations of Rule Learning. Cognitive Technologies, Springer, Cham (2012). https://doi.org/10.1007/978-3-540-75197-7

    Book  Google Scholar 

  16. Gilmore, E., Estivill-Castro, V., Hexel, R.: More interpretable decision trees. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds.) HAIS 2021. LNCS (LNAI), vol. 12886, pp. 280–292. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-86271-8_24

    Chapter  Google Scholar 

  17. Hartmanis, J., Hemachandra, L.A.: One-way functions, robustness, and the non-isomorphism of NP-complete sets. In: Proceedings of the Second Annual Conference on Structure in Complexity Theory, Cornell University, Ithaca, New York, USA, 16–19 June 1987. IEEE Computer Society (1987)

    Google Scholar 

  18. Imam, I.F., Michalski, R.S.: Learning decision trees from decision rules: a method and initial results from a comparative study. J. Intell. Inf. Syst. 2(3), 279–304 (1993)

    Article  Google Scholar 

  19. Imam, I.F., Michalski, R.S.: Should decision trees be learned from examples or from decision rules? In: Komorowski, J., Raś, Z.W. (eds.) ISMIS 1993. LNCS, vol. 689, pp. 395–404. Springer, Heidelberg (1993). https://doi.org/10.1007/3-540-56804-2_37

    Chapter  Google Scholar 

  20. Imam, I.F., Michalski, R.S.: Learning for decision making: the FRD approach and a comparative study. In: Raś, Z.W., Michalewicz, M. (eds.) ISMIS 1996. LNCS, vol. 1079, pp. 428–437. Springer, Heidelberg (1996). https://doi.org/10.1007/3-540-61286-6_167

    Chapter  Google Scholar 

  21. Kaufman, K.A., Michalski, R.S., Pietrzykowski, J., Wojtusiak, J.: An integrated multi-task inductive database VINLEN: initial implementation and early results. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 116–133. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-75549-4_8

    Chapter  Google Scholar 

  22. Michalski, R.S., Imam, I.F.: Learning problem-oriented decision structures from decision rules: the AQDT-2 system. In: Raś, Z.W., Zemankova, M. (eds.) ISMIS 1994. LNCS, vol. 869, pp. 416–426. Springer, Heidelberg (1994). https://doi.org/10.1007/3-540-58495-1_42

    Chapter  Google Scholar 

  23. Michalski, R.S., Imam, I.F.: On learning decision structures. Fundam. Informaticae 31(1), 49–64 (1997)

    Article  Google Scholar 

  24. Molnar, C.: Interpretable Machine Learning. A Guide for Making Black Box Models Explainable, 2nd edn. (2022). https://christophm.github.io/interpretable-ml-book/

  25. Moshkov, M.J.: Comparative analysis of deterministic and nondeterministic decision tree complexity local approach. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets IV. LNCS, vol. 3700, pp. 125–143. Springer, Heidelberg (2005). https://doi.org/10.1007/11574798_7

    Chapter  Google Scholar 

  26. Moshkov, M.J.: Time complexity of decision trees. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 244–459. Springer, Heidelberg (2005). https://doi.org/10.1007/11427834_12

    Chapter  Google Scholar 

  27. Moshkov, M.: About the depth of decision trees computing Boolean functions. Fundam. Informaticae 22(3), 203–215 (1995)

    Article  MathSciNet  Google Scholar 

  28. Moshkov, M.: Comparative analysis of deterministic and nondeterministic decision tree complexity. Global approach. Fundam. Informaticae 25(2), 201–214 (1996)

    Article  MathSciNet  Google Scholar 

  29. Moshkov, M.: Some relationships between decision trees and decision rule systems. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS (LNAI), vol. 1424, pp. 499–505. Springer, Heidelberg (1998). https://doi.org/10.1007/3-540-69115-4_68

    Chapter  Google Scholar 

  30. Moshkov, M.: Deterministic and nondeterministic decision trees for rough computing. Fundam. Informaticae 41(3), 301–311 (2000)

    Article  MathSciNet  Google Scholar 

  31. Moshkov, M.: On transformation of decision rule systems into decision trees. In: Proceedings of the Seventh International Workshop Discrete Mathematics and its Applications, Moscow, Russia, 29 January–2 February 2001, Part 1, pp. 21–26. Center for Applied Investigations of Faculty of Mathematics and Mechanics, Moscow State University (2001). (in Russian)

    Google Scholar 

  32. Moshkov, M.: Classification of infinite information systems depending on complexity of decision trees and decision rule systems. Fundam. Informaticae 54(4), 345–368 (2003)

    MathSciNet  Google Scholar 

  33. Moshkov, M.: Comparative Analysis of Deterministic and Nondeterministic Decision Trees. ISRL, vol. 179. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-41728-4

    Book  Google Scholar 

  34. 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, Cham (2008). https://doi.org/10.1007/978-3-540-69029-0

    Book  Google Scholar 

  35. Moshkov, M., Zielosko, B.: Combinatorial Machine Learning - A Rough Set Approach. Studies in Computational Intelligence, vol. 360. Springer, Cham (2011). https://doi.org/10.1007/978-3-642-20995-6

    Book  Google Scholar 

  36. Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning about Data, Theory and Decision Library: Series D, vol. 9. Kluwer (1991)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  38. Quinlan, J.R.: Generating production rules from decision trees. In: McDermott, J.P. (ed.) Proceedings of the 10th International Joint Conference on Artificial Intelligence. Milan, Italy, 23–28 August 1987, pp. 304–307. Morgan Kaufmann (1987)

    Google Scholar 

  39. Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann (1993)

    Google Scholar 

  40. Quinlan, J.R.: Simplifying decision trees. Int. J. Hum Comput Stud. 51(2), 497–510 (1999)

    Article  Google Scholar 

  41. Rokach, L., Maimon, O.: Data Mining with Decision Trees - Theory and Applications, Series in Machine Perception and Artificial Intelligence, vol. 69. World Scientific (2007)

    Google Scholar 

  42. Silva, A., Gombolay, M.C., Killian, T.W., Jimenez, I.D.J., Son, S.: Optimization methods for interpretable differentiable decision trees applied to reinforcement learning. In: Chiappa, S., Calandra, R. (eds.) The 23rd International Conference on Artificial Intelligence and Statistics, AISTATS 2020, Palermo, Sicily, Italy, 26–28 August 2020. Proceedings of Machine Learning Research, vol. 108, pp. 1855–1865. PMLR, Online (2020)

    Google Scholar 

  43. Szydlo, T., Sniezynski, B., Michalski, R.S.: A rules-to-trees conversion in the inductive database system VINLEN. In: Klopotek, M.A., Wierzchon, S.T., Trojanowski, K. (eds.) Intelligent Information Processing and Web Mining Advances in Soft Computing, vol. 31, pp. 496–500. Springer, Cham (2005). https://doi.org/10.1007/3-540-32392-9_60

    Chapter  Google Scholar 

  44. Tardos, G.: Query complexity, or why is it difficult to separate \({NP}^{A}\cap co{NP}^{A}\) from \({P}^{A}\) by random oracles \({A}\)? Combinatorica 9(4), 385–392 (1989)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

Research reported in this publication was supported by King Abdullah University of Science and Technology (KAUST).

Author information

Authors and Affiliations

  1. Computer, Electrical and Mathematical Sciences and Engineering Division and Computational Bioscience Research Center, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia

    Kerven Durdymyradov & Mikhail Moshkov

Authors
  1. Kerven Durdymyradov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mikhail Moshkov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kerven Durdymyradov .

Editor information

Editors and Affiliations

  1. Saint Mary's University, Halifax, NS, Canada

    Mengjun Hu

  2. Ghent University, Ghent, Belgium

    Chris Cornelis

  3. California State University, San Bernardino, CA, USA

    Yan Zhang

  4. Saint Mary's University, Halifax, NS, Canada

    Pawan Lingras

  5. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  6. University of Regina, Regina, SK, Canada

    JingTao Yao

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Durdymyradov, K., Moshkov, M. (2024). Simulating Functioning of Decision Trees for Tasks on Decision Rule Systems. In: Hu, M., Cornelis, C., Zhang, Y., Lingras, P., Ślęzak, D., Yao, J. (eds) Rough Sets. IJCRS 2024. Lecture Notes in Computer Science(), vol 14839. Springer, Cham. https://doi.org/10.1007/978-3-031-65665-1_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-65665-1_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-65664-4

  • Online ISBN: 978-3-031-65665-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics