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Aggregation framework for TSK fuzzy and association rules: interpretability improvement on a traffic accidents case

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Abstract

The number and diversity of machine learning applications causes an increasing need for understanding computational models and used data. This paper deals with a framework design of easily interpretable rules of the Takagi-Sugeno-Kang (TSK) fuzzy model. The proposed framework aggregates TSK fuzzy rules and association rules by calculating overlapping value intervals of variables appearing in both antecedent and consequent parts of fuzzy and association rules. Besides a simple insight into rule interconnections of the rule-based models, the framework provides an assessment of fuzzy rule importance, and in accordance with other rules and the complete TSK fuzzy model. The proposed framework is developed and illustrated by analysing traffic accidents with pedestrian involvement. It provides a deeper understanding of the built rule-based model, as well as more readable identification of significant accident causes. The framework can be used in many domains of analysis modelling and decision making processes where computational model understanding is crucial.

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References

  1. Lee CC (1990) Fuzzy logic in control systems: fuzzy logic controller - part I and II. IEEE Trans Syst Man Cybern 20(2):404–435

    Article  MathSciNet  Google Scholar 

  2. Kukolj D (2002) Design of adaptive Takagi–Sugeno–Kang fuzzy models. Appl Soft Comput 2:89–103. https://doi.org/10.1016/S1568-4946(02)00032-7

    Article  Google Scholar 

  3. Magdalena L (2018) Designing interpretable Hierarchical Fuzzy Systems. IEEE Int. Conf. on Fuzzy Systems (FUZZ-IEEE) pp 1–8 doi: https://doi.org/10.1109/FUZZ-IEEE.2018.8491452

  4. Mencar C, Castellano G, Finelli AM (2005) Some Fundamental Interpretability Issues in Fuzzy Modeling. Joint EUSFLAT-LFA Barcelona, Spain, September 7–9 pp 100–105

  5. Zhou T, Ishibuchi H, Wang S (2017) Stacked-structure-based hierarchical Takagi-Sugeno-Kang fuzzy classification through feature augmentation. IEEE Trans Emerging Top Comput Intell 1(6):421–436

    Article  Google Scholar 

  6. Zhang Y, Ishibuchi H, Wang S (2018) Deep Takagi–Sugeno–Kang fuzzy classifier with shared linguistic fuzzy rules. IEEE Trans Fuzzy Syst 26(3):1535–1549. https://doi.org/10.1109/TFUZZ.2017.2729507

    Article  Google Scholar 

  7. Riid A, Rüstern E (2011) Identification of transparent, compact, accurate and reliable linguistic fuzzy models. Inf Sci 181(20):4378–4393. https://doi.org/10.1016/j.ins.2011.01.041

    Article  MATH  Google Scholar 

  8. Ho WL, Tong WL (2010) Quek C (2010) an evolving Mamdani-Takagi-Sugeno based neural-fuzzy inference system with improved interpretability-accuracy. FUZZ-IEEE. https://doi.org/10.1109/FUZZY.2010.5584831

  9. Zhou SM, Gan JQ (2009) Extracting Takagi-Sugeno fuzzy rules with interpretable submodels via regularization of linguistic modifiers. IEEE Trans Knowl Data Eng 21(8):1191–1204. https://doi.org/10.1109/TKDE.2008.208

    Article  Google Scholar 

  10. Driss M, Saint-Gerand T, Bensaid A, Benabdeli K, Hamadouche MA (2013) A fuzzy logic model for identifying spatial degrees of exposure to the risk of road accidents. Int. Conf. Advanced Logistics and Transport (ICALT) Sousse, pp 69–74

  11. Wahaballa AM, Diab A, Gaber M, Othman AM (2017) Sensitivity of Traffic Accidents Mitigation Policies Based on Fuzzy Modeling: A Case Study. Proc. IEEE 20th Int. Conf. on Intelligent Transportation Systems (ITSC) pp 45–50

  12. Hosseinpour M, Yahaya AS, Ghadiri SM, Prasetijo J (2013) Application of adaptive neuro-fuzzy inference system for road accident prediction. KSCE J Civ Eng 17(7):1761–1772. https://doi.org/10.1007/s12205-013-0036-3

    Article  Google Scholar 

  13. Yong L, Shibo Z (2009) The fuzzy regression prediction of the City road traffic accident. Int Conf on Indust Mech Autom:121–124

  14. Bin C, Yong L (2009) The road safety prediction model based on the fuzzy linear regression. Int Conf on Comput Intel Nat Comput:19–21

  15. Wang R, Chen Y, Li T, Li P, Sun J (2013) Classification of road safety based on fuzzy clustering. Proc IEEE 10th Int Conf Fuzzy Syst Knowl Discov (FSKD):354–358

  16. Weng J, Zhu JZ, Yan X, Liu Z (2016) Investigation of work zone crash casualty patterns using association rules. Accid Anal Prev 92:92–43

    Article  Google Scholar 

  17. Gao Z, Pan R, Wang X, Yu R (2018) Research on automated modeling algorithm using association rules for traffic accidents. Proc IEEE Int Conf Big DataSmart Comput:127–132

  18. Äyrämö S, Pirtala P, Kauttonen J, Naveed K, Karkkainen T (2009) Mining road traffic accidents. Technical Report, Reports of the Department of Mathematical Information Technology Series C. Software and Computational Engineering No. C. 2/2009 http://users.jyu.fi/~samiayr/pdf/mining_road_traffic_accidents.pdf

  19. Kumar S, Toshniwal D (2015) Analysing Road Accident Data Using Association Rule Mining. Proc. of Int. Conf. on Computing Communication and Security pp 1–6

  20. Viharos ZJ, Kis KB (2016) Optimal Neuro-Fuzzy model configuration. IEEE Int Conf on Syst, Man, Cybernet. https://doi.org/10.1109/SMC.2016.7844799

  21. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proc IEEE Int Conf Neural Netw 4:1942–1948. https://doi.org/10.1109/ICNN.1995.488968

    Article  Google Scholar 

  22. Zhao Q, Bhowmick SS (2003) Association Rule Mining: A Survey. Technical Report, CAIS Nanyang Technological University Singapore

  23. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. Proc. of 20th Int. Conf. Very Large Data Bases, pp 487–499

  24. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its application to modelling and control. IEEE Trans Syst Man Cybern 15:116–132

    Article  Google Scholar 

  25. Kukolj D, Levi E (2004) Identification of complex systems based on neural and Takagi-Sugeno fuzzy model. IEEE Trans Syst Man Cybern -part B 34(1):272–282

    Article  Google Scholar 

  26. Kukolj D, Atlagić B, Petrov M (2006) Unlabeled data clustering using a re-organizing neural network. Cybern Syst Int J 37(7):779–790. https://doi.org/10.1080/01969720600887152

    Article  MATH  Google Scholar 

  27. Jang JR, Sun C, Mizutani E (1997) Neuro-fuzzy and soft computing. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  28. Golub GH, Van Loan CF (1989) Matrix computations. Johns Hopkins Univ. Press Hall, Baltimore

    MATH  Google Scholar 

  29. Bulajić A, Jovanović D, Matović B, Bačkalić SD (2014) Identification of high-density locations with homogenous attributes of pedestrian accident in the urban area of Novi Sad. XII Int. Symposium, Road Accidents Preventions 2014:89–98

  30. Alonso JM, Magdalena L (2011) HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft Comput 15(10):1959–1980. https://doi.org/10.1007/s00500-010-0628-5

    Article  Google Scholar 

  31. Nauck DD (2003) Measuring interpretability in rule-based classification systems. Proc. of 12th IEEE INT CONF FUZZY’03, pp 196–201

  32. Pancho DP, Alonso JM, Magdalena L (2013) Quest for interpretability-accuracy trade-off supported by Fingrams into the fuzzy modeling tool GUAJE. Int J Comput Intel Syst 6(1):46–60. https://doi.org/10.1080/18756891.2013.818189

    Article  Google Scholar 

  33. Alonso JM, Magdalena L (2011) Generating understandable and accurate fuzzy rule-based systems in a java environment, LECT NOTES ARTIF INT - 9th Int. Workshop on Fuzzy Logic and Applications, LNAI6857 pp 212–219 DOI:https://doi.org/10.1007/978-3-642-23713-3_27

    Chapter  Google Scholar 

  34. Wang LX, Mendel JM (1992) Generating fuzzy rules by learning from examples. IEEE Trans Syst Man Cybern 22(6):1414–1427

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was partially supported by the Ministry of Education, Science and Technology Development of the Republic of Serbia under the Grant TR32034.

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Authors and Affiliations

  1. RT-RK Institute, Novi Sad, Serbia

    Sandra Nemet

  2. Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia

    Dragan Kukolj, Gordana Ostojić, Stevan Stankovski & Dragan Jovanović

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  1. Sandra Nemet
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  2. Dragan Kukolj
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  3. Gordana Ostojić
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  4. Stevan Stankovski
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  5. Dragan Jovanović
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Correspondence to Sandra Nemet.

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Nemet, S., Kukolj, D., Ostojić, G. et al. Aggregation framework for TSK fuzzy and association rules: interpretability improvement on a traffic accidents case. Appl Intell 49, 3909–3922 (2019). https://doi.org/10.1007/s10489-019-01485-6

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  • DOI: https://doi.org/10.1007/s10489-019-01485-6

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