Skip to main content
SpringerLink
Log in

SQ-FMFO: A Novel Scalarized Multi-objective Q-Learning Approach for Fuzzy Membership Function Optimization

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Association Rule mining plays a crucial role in data mining by extracting relationships among the data items in the form of association rules. Fuzzy association rule mining is one of the major techniques in rule mining, and it often provides better rules and flexibility over quantitative rule mining by utilizing fuzzy set theory. The outcome of fuzzy association rule mining is greatly affected by the appropriateness of its membership functions. In this research, we have provided a comprehensive description of fuzzy membership function optimization as a Markov decision process (MDP) as well as introduced two novel algorithms- one for membership function optimization and the other for choosing the optimal number of membership functions for any given problem. This study proposes a novel multi-objective reinforcement learning technique that incorporates scalarization of different value functions while determining optimal policy and is able to construct optimized membership functions with better suitability and coverage. The proposed multi-objective reinforcement learning algorithm outperforms well-known techniques in fuzzy membership function optimization by utilizing the exploration and exploitation mechanism. The performance of the proposed techniques is clearly validated by the experimental analysis incorporated in this study and is the first study that concisely treats fuzzy membership function optimization in terms of an MDP environment it opens up enumerable opportunities for future reinforcement learning endeavors.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17, 37–54 (1996). https://doi.org/10.1609/aimag.v17i3.1230

    Article  Google Scholar 

  2. Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6), 866–883 (1996). https://doi.org/10.1109/69.553155

    Article  Google Scholar 

  3. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994).

  4. Kabir, M.M.J., Xu, S., Kang, B.H., Zhao, Z.: A new multiple seeds based genetic algorithm for discovering a set of interesting Boolean association rules. Expert Syst. Appl. 74, 55–69 (2017). https://doi.org/10.1016/j.eswa.2017.01.001

    Article  Google Scholar 

  5. Kabir, M.M.J., Xu, S., Kang, B.H., Zhao, Z.: Discovery of interesting association rules using genetic algorithm with adaptive mutation. Int. Conf. Neural Inf. Process 9490, 96–105 (2015). https://doi.org/10.1007/978-3-319-26535-3_12

    Article  Google Scholar 

  6. Kabir, M.J., Xu, S., Kang, B.H.: A new evolutionary algorithm for extracting a reduced set of interesting association rules. Int. Conf. Neural Inf. Process. (2015). https://doi.org/10.1007/978-3-319-26535-3

    Article  Google Scholar 

  7. Badhon, B., Kabir, M.M.J., Xu, S., Kabir, M.: A survey on association rule mining based on evolutionary algorithms. Int. J. Comput. Appl. (2019). https://doi.org/10.1080/1206212X.2019.1612993

    Article  Google Scholar 

  8. Srikant, R., Agrawal, R.: Mining quantitative association rules in large relational tables. SIGMOD Rec. 25(2), 1–12 (1996). https://doi.org/10.1145/235968.233311

    Article  Google Scholar 

  9. Hong, T.P., Chen, C.H., Lee, Y.C., Wu, Y.L.: Genetic-fuzzy data mining with divide-and-conquer strategy. IEEE Trans. Evol. Comput. 12(2), 252–265 (2008). https://doi.org/10.1109/TEVC.2007.900992

    Article  Google Scholar 

  10. Hong, T.P., Kuo, C.S., Chi, S.C.: Mining association rules from quantitative data. Intell. Data Anal. 3(5), 363–376 (1999). https://doi.org/10.3233/IDA-1999-3504

    Article  MATH  Google Scholar 

  11. Wang, W., Bridges, S.M.: Genetic algorithm optimization of membership functions for mining fuzzy association rules. Proc. Jt. Conf. Inf. Sci. 5(1), 131–134 (2000)

    Google Scholar 

  12. Chai, C., Li, B.: A novel association rules method based on genetic algorithm and fuzzy set strategy for web mining. J. Comput. 5(9), 1448–1455 (2010). https://doi.org/10.4304/jcp.5.9.1448-1455

    Article  Google Scholar 

  13. Chen, C., Li, Y., Hong, T., Li, Y.: A GA-based approach for mining membership functions and concept-drift patterns. In: 2015 IEEE Congress on Evolutionary Computation (CEC), vol. 2, no. 1, pp. 2961–2965 (2015).

  14. Chen, C., Li, Y., Hong, T.: Type-2 genetic-fuzzy mining with tuning mechanism. In: 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 296–299 (2015).

  15. Alcalá-fdez, J., Alcalá, R., Gacto, M.J., Herrera, F.: Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Set Syst. 160, 905–921 (2009). https://doi.org/10.1016/j.fss.2008.05.012

    Article  MathSciNet  MATH  Google Scholar 

  16. María, A., Luis, J., Sánchez, L., Alcalá-fdez, J.: Genetic learning of the membership functions for mining fuzzy association rules from low quality data. Inf. Sci. (2014). https://doi.org/10.1016/j.ins.2014.10.027

    Article  MATH  Google Scholar 

  17. Kaya, M., Alhajj, R.: Genetic algorithms based optimization of membership functions for fuzzy weighted association rules mining. In: In: Proceedings of ISCC 2004, Ninth International Symposium on Computers and Communications, pp. 110–115.

  18. Ting, C., Hong, R.L.T.W.T.: Mining fuzzy association rules using a Memetic algorithm based on structure representation. Memetic Comput. (2017). https://doi.org/10.1007/s12293-016-0220-3

    Article  Google Scholar 

  19. Wang, C.T.T., Hong, R.L.T.: Genetic algorithm with a structure-based representation for genetic-fuzzy data mining. Soft Comput. (2016). https://doi.org/10.1007/s00500-016-2266-z

    Article  Google Scholar 

  20. Rudzinski, F.: A multi-objective genetic optimization of interpretability-oriented fuzzy rule-based classifiers. Appl. Soft Comput. (2015). https://doi.org/10.1016/j.asoc.2015.09.038

    Article  Google Scholar 

  21. Hong, T.P., Lee, Y.C., Wu, M.T.: An effective parallel approach for genetic-fuzzy data mining. Expert Syst. Appl. 41(2), 655–662 (2014). https://doi.org/10.1016/j.eswa.2013.07.090

    Article  Google Scholar 

  22. Song, A., Song, J., Ding, X., Xu, G.: Utilizing bat algorithm to optimize membership functions for fuzzy association rules mining. In: International conference on database and expert systems applications, vol. 1, pp. 496–504 (2017).

  23. Chamazi, M.A., Motameni, H.: Finding suitable membership functions for fuzzy temporal mining problems using fuzzy temporal bees method. Soft Comput. 23(10), 3501–3518 (2019). https://doi.org/10.1007/s00500-018-3010-7

    Article  Google Scholar 

  24. Anari, Z., Hatamlou, A., Masdari, M.: CALA-FOMF: a continuous action-set learning automata-based approach to finding optimized membership functions for fuzzy association rules in web usage data. Soft Comput. 24(23), 18089–18112 (2020). https://doi.org/10.1007/s00500-020-05064-7

    Article  Google Scholar 

  25. Agrawal, R., Imielinski, T., Swami, A.: Mining association in large databases. In: Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data - SIGMOD ’93, pp. 207–216 (1993).

  26. Wu, T.H.C.C.Y., Lee, Y.: A GA-based fuzzy mining approach to achieve a trade-off between number of rules and suitability of membership functions. Soft Comput. 10(11), 1091–1101 (2006). https://doi.org/10.1007/s00500-006-0046-x

    Article  Google Scholar 

  27. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  28. Watkins, C.J., Dayan, P.: Q-learning. Mach. Learn. 8(3), 279–292 (1992)

    Article  MATH  Google Scholar 

  29. Van Moffaert, K., Nowé, A.: Multi-objective reinforcement learning using sets of Pareto dominating policies. J. Mach. Learn. Res. 1, 3663–3692 (2014)

    MathSciNet  MATH  Google Scholar 

  30. Vamplew, P., Yearwood, J., Dazeley, R., Berry, A.: On the limitations of scalarisation for multi-objective reinforcement learning of Pareto fronts 2 reviewing existing approaches to MORL. In: Australasian Joint Conference on Artificial Intelligence, pp. 372–378 (2008).

  31. Van Moffaert, K., Drugan, M.M., Nowe, A.: Scalarized multi-objective reinforcement learning: Novel design techniques. IEEE Symp. Adapt. Dyn. Program. Reinf. Learn. ADPRL (2013). https://doi.org/10.1109/ADPRL.2013.6615007

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

    Bodrunnessa Badhon, Mir Md. Jahangir Kabir & Md. Asifur Rahman

  2. School of ICT, University of Tasmania, Hobart, Australia

    Shuxiang Xu

Authors
  1. Bodrunnessa Badhon
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mir Md. Jahangir Kabir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Md. Asifur Rahman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shuxiang Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bodrunnessa Badhon.

Ethics declarations

Conflict of interest

Not applicable.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Badhon, B., Kabir, M.M.J., Rahman, M.A. et al. SQ-FMFO: A Novel Scalarized Multi-objective Q-Learning Approach for Fuzzy Membership Function Optimization. Int. J. Fuzzy Syst. 25, 633–646 (2023). https://doi.org/10.1007/s40815-022-01381-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-022-01381-1

Keywords

Search

Navigation