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A multi-stage learning-based fuzzy cognitive maps for tobacco use

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Abstract

Fuzzy cognitive map (FCM) is an important approach for modeling the behavior of dynamic systems. FCM’s ability to represent casual relationships between the concepts (factors, attributes, etc.) has attracted the interest of researchers from different disciplines. The construction process of FCMs is mostly initialized with expert knowledge because FCMs can conveniently incorporate available information and expertise in the determination of vital parameters and relations of the system. However, their higher dependence on expert knowledge may significantly influence the reliability of the model due to the increase in subjectivity. In order to avoid weaknesses depending on expert knowledge, learning algorithms that search for the appropriate relationships between the concepts have been used with FCM studies. In this paper, a FCM analysis was performed for tobacco use to understand the cause–effect relationships between demographic characteristics of people (such as gender, age range, and residence type) and likelihood to tobacco use. In order to reduce the impact of external interventions (from experts), a multi-stage learning procedure was applied by integrating two different learning algorithms (nonlinear Hebbian learning algorithm and extended Great Deluge algorithm). The results showed that the multi-stage learning procedure increased the accuracy of the model and provided more reliable maps for the studied system. They also proved that the multi-stage learning procedures can help to reduce the dependency to expert knowledge and improve the robustness of the study.

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References

  1. System, Definition of system in English by Oxford Dictionaries. In: Oxford Dictionaries|English. https://en.oxforddictionaries.com/definition/system. Accessed 11 Dec 2018

  2. System, meaning in the Cambridge English Dictionary. https://dictionary.cambridge.org/dictionary/english/system. Accessed 11 Dec 2018

  3. Miller JG (1995) Living systems. University Press of Colorado, Niwot

    Google Scholar 

  4. Backlund A (2000) The definition of system. Kybernetes 29:444–451. https://doi.org/10.1108/03684920010322055

    Article  MATH  Google Scholar 

  5. Papakostas GA, Polydoros AS, Koulouriotis DE, Tourassis VD (2011) Training fuzzy cognitive maps by using Hebbian learning algorithms: a comparative study. In: 2011 IEEE international conference on fuzzy systems (FUZZ-IEEE 2011), pp 851–858

  6. Papageorgiou EI, Salmeron JL (2013) A review of fuzzy cognitive maps research during the last decade. IEEE Trans Fuzzy Syst 21:66–79. https://doi.org/10.1109/TFUZZ.2012.2201727

    Article  Google Scholar 

  7. Stach W, Kurgan L, Pedrycz W (2008) Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps. In: 2008 IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence). pp 1975–1981

  8. Papageorgiou EI, Stylios C, Groumpos PP (2006) Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. Int J Hum Comput Stud 64:727–743. https://doi.org/10.1016/j.ijhcs.2006.02.009

    Article  Google Scholar 

  9. Stach W, Kurgan L (2004) Parallel fuzzy cognitive maps as a tool for modeling software development projects. In: IEEE annual meeting of the fuzzy information, 2004 processing NAFIPS’04, vol 1, pp 28–33

  10. Styblinski MA, Meyer BD (1991) Signal flow graphs vs fuzzy cognitive maps in application to qualitative circuit analysis. Int J Man Mach Stud 35:175–186. https://doi.org/10.1016/S0020-7373(05)80147-6

    Article  Google Scholar 

  11. Senniappan V, Subramanian J, Papageorgiou EI, Mohan S (2017) Application of fuzzy cognitive maps for crack categorization in columns of reinforced concrete structures. Neural Comput Appl 28:107–117. https://doi.org/10.1007/s00521-016-2313-9

    Article  Google Scholar 

  12. Beena P, Ganguli R (2011) Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl Soft Comput 11:1014–1020. https://doi.org/10.1016/j.asoc.2010.01.023

    Article  Google Scholar 

  13. Amer M, Jetter AJ, Daim TU (2013) Scenario planning for the national wind energy sector through Fuzzy Cognitive Maps. In: 2013 Proceedings of PICMET’13: technology management in the IT-Driven Services (PICMET). pp 2153–2162

  14. Kyriakarakos G, Dounis AI, Arvanitis KG, Papadakis G (2012) A fuzzy cognitive maps–petri nets energy management system for autonomous polygeneration microgrids. Appl Soft Comput 12:3785–3797. https://doi.org/10.1016/j.asoc.2012.01.024

    Article  Google Scholar 

  15. Stylios CD, Georgopoulos VC, Malandraki GA, Chouliara S (2008) Fuzzy cognitive map architectures for medical decision support systems. Appl Soft Comput 8:1243–1251. https://doi.org/10.1016/j.asoc.2007.02.022

    Article  Google Scholar 

  16. Giabbanelli PJ, Torsney-Weir T, Mago VK (2012) A fuzzy cognitive map of the psychosocial determinants of obesity. Appl Soft Comput 12:3711–3724. https://doi.org/10.1016/j.asoc.2012.02.006

    Article  Google Scholar 

  17. Papageorgiou EI, Papandrianos NI, Apostolopoulos DJ, Vassilakos PJ (2008) Fuzzy cognitive map based decision support system for thyroid diagnosis management. In: 2008 IEEE international conference on fuzzy systems (IEEE world congress on computational intelligence), pp 1204–1211

  18. Mago VK, Mehta R, Woolrych R, Papageorgiou EI (2012) Supporting meningitis diagnosis amongst infants and children through the use of fuzzy cognitive mapping. BMC Med Inform Decis Mak 12:98. https://doi.org/10.1186/1472-6947-12-98

    Article  Google Scholar 

  19. Kannappan A, Tamilarasi A, Papageorgiou EI (2011) Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder. Expert Syst Appl 38:1282–1292. https://doi.org/10.1016/j.eswa.2010.06.069

    Article  Google Scholar 

  20. Papageorgiou EI, Spyridonos PP, Glotsos DTh et al (2008) Brain tumor characterization using the soft computing technique of fuzzy cognitive maps. Appl Soft Comput 8:820–828. https://doi.org/10.1016/j.asoc.2007.06.006

    Article  Google Scholar 

  21. Papageorgiou EI, Froelich W (2012) Application of evolutionary fuzzy cognitive maps for prediction of pulmonary infections. IEEE Trans Inf Technol Biomed 16:143–149. https://doi.org/10.1109/TITB.2011.2175937

    Article  Google Scholar 

  22. Tsadiras AK (2003) Using fuzzy cognitive Maps for e-commerce strategic planning in 2003 proceedings of the 9th panhellenic conference on informatics

  23. Schläger C, Pernul G (2008) Trust modelling in e-commerce through fuzzy cognitive maps. In: 2008 third international conference on availability, reliability and security, pp 344–351

  24. Kardaras, Mentzas G, Mentzas G, Mentzas G (1997) Using fuzzy cognitive maps to model and analyse business performance assessment and advances in industrial engineering applications and practice II, pp 63–68

  25. Baykasoglu A, Durmusoglu ZDU, Kaplanoglu V (2011) Training fuzzy cognitive maps via extended great deluge algorithm with applications. Comput Ind 62:187–195. https://doi.org/10.1016/j.compind.2010.10.011

    Article  Google Scholar 

  26. Jahangoshai Rezaee M, Yousefi S, Hayati J (2018) A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level. Neural Comput Appl 29:761–777. https://doi.org/10.1007/s00521-016-2478-2

    Article  Google Scholar 

  27. Dickerson JA, Kosko B (1994) Virtual worlds as fuzzy cognitive maps. presence: teleoperators and virtual environments vol 3, pp 173–189. https://doi.org/10.1162/pres.1994.3.2.173

  28. Huerga AV (2002) A balanced differential learning algorithm in fuzzy cognitive maps. In: Proceedings of 16th international workshop on qualitative reasoning

  29. Papageorgiou E, Stylios C, Groumpos P (2003) Fuzzy cognitive map learning based on nonlinear hebbian rule. In: Gedeon T (Tom) D, Fung LCC (eds) AI 2003: advances in artificial intelligence. Springer, Berlin, pp 256–268

  30. Papageorgiou EI, Stylios CD, Groumpos PP (2004) Active Hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reason 37:219–249. https://doi.org/10.1016/j.ijar.2004.01.001

    Article  MathSciNet  MATH  Google Scholar 

  31. Ren Z (2012) Learning fuzzy cognitive maps by a hybrid method using nonlinear hebbian learning and extended great deluge algorithm. In: MAICS

  32. Koulouriotis DE, Diakoulakis IE, Emiris DM (2001) Anamorphosis of fuzzy cognitive maps for operation in ambiguous and multi-stimulus real world environments. In: 10th IEEE international conference on fuzzy systems. (Cat. No.01CH37297), vol 2, pp 1156–1159

  33. Papageorgiou EI, Parsopoulos KE, Groumpos PP, Vrahatis MN (2004) Fuzzy cognitive maps learning through swarm intelligence. In: Rutkowski L, Siekmann JH, Tadeusiewicz R, Zadeh LA (eds) Artificial intelligence and soft computing—ICAISC 2004. Springer, Berlin, pp 344–349

    Chapter  Google Scholar 

  34. Papageorgiou EI, Parsopoulos KE, Stylios CS et al (2005) Fuzzy cognitive maps learning using particle swarm optimization. J Intell Inf Syst 25:95–121. https://doi.org/10.1007/s10844-005-0864-9

    Article  Google Scholar 

  35. Song H, Miao C, Shen Z, Miao Y (2008) Fuzzy cognitive map learning based on multi-objective PSO (Invited Paper)

  36. Stach W, Kurgan L, Pedrycz W, Reformat M (2005) Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst 153:371–401. https://doi.org/10.1016/j.fss.2005.01.009

    Article  MathSciNet  MATH  Google Scholar 

  37. Ghazanfari M, Alizadeh S, Fathian M, Koulouriotis DE (2007) Comparing simulated annealing and genetic algorithm in learning FCM. Appl Math Comput 192:56–68. https://doi.org/10.1016/j.amc.2007.02.144

    Article  MathSciNet  MATH  Google Scholar 

  38. Alizadeh S, Ghazanfari M, Jafari M, Hooshmand S (2007) Learning FCM by Tabu search. Int J Comput Sci 3:142–149

    Google Scholar 

  39. Durmusoglu ZDU, Ciftci PK (2014) A study of factors affecting the tobacco use. In: Joint symposium on 44th computers & industrial engineering (CIE44) & 9th intelligent manufacturing and service systems (IMSS14), pp 1287–1295

  40. Ahmadi S, Forouzideh N, Alizadeh S, Papageorgiou E (2015) Learning fuzzy cognitive maps using imperialist competitive algorithm. Neural Comput Appl 26:1333–1354. https://doi.org/10.1007/s00521-014-1797-4

    Article  Google Scholar 

  41. Papageorgiou EI, Groumpos PP (2005) A new hybrid method using evolutionary algorithms to train Fuzzy Cognitive Maps. Appl Soft Comput 5:409–431. https://doi.org/10.1016/j.asoc.2004.08.008

    Article  Google Scholar 

  42. Zhu Y, Zhang W (2008) An integrated framework for learning fuzzy cognitive map using RCGA and NHL Algorithm. In: 2008 4th international conference on wireless communications, networking and mobile computing, pp 1–5

  43. Alizadeh A, Yousefi S (2018) An integrated Taguchi loss function–fuzzy cognitive map–MCGP with utility function approach for supplier selection problem. Neural Comput Appl. https://doi.org/10.1007/s00521-018-3591-1

    Article  Google Scholar 

  44. WHO, Tobacco. In: WHO. http://www.who.int/mediacentre/factsheets/fs339/en/. Accessed 8 Sep 2015

  45. Mago VK, Morden HK, Fritz C et al (2013) Analyzing the impact of social factors on homelessness: a fuzzy cognitive map approach. BMC Med Inform Decis Mak 13:94. https://doi.org/10.1186/1472-6947-13-94

    Article  Google Scholar 

  46. WHO, Global Adult Tobacco Survey (GATS). In: WHO. http://www.who.int/tobacco/surveillance/survey/gats/en/. Accessed 14 Jun 2016

  47. What is a “Chi Square Test”? https://www.spss-tutorials.com/Chisquare-independence-test/. Accessed 13 Dec 2018

  48. Using Chi Square Statistic in Research. In: Statistics Solutions. https://www.statisticssolutions.com/using-Chisquare-statistic-in-research/. Accessed 13 Dec 2018

  49. Banks J, Carson JS, Nelson BL, Nicol DM (2004) Discrete-event system simulation. TBS

  50. Peters G, Crespo F, Lingras P, Weber R (2013) Soft clustering—fuzzy and rough approaches and their extensions and derivatives. Int J Approx Reason 54:307–322. https://doi.org/10.1016/j.ijar.2012.10.003

    Article  MathSciNet  Google Scholar 

  51. Hatamlou A, Abdullah S, Nezamabadi-pour H (2011) Application of gravitational search algorithm on data clustering. In: Yao J, Ramanna S, Wang G, Suraj Z (eds) Rough sets and knowledge technology. Springer, Berlin, pp 337–346

    Chapter  Google Scholar 

  52. Olson DL, Shi Y (2007) Introduction to Business Data Mining. McGraw Hill

  53. Cluster Analysis. http://www.ccs.neu.edu/home/futrelle/teaching/isu535sp2004/finalpapers/clusteringIntro.html. Accessed 13 Dec 2018

  54. Baek S, Tsai C-A, Chen JJ (2009) Development of biomarker classifiers from high-dimensional data. Brief Bioinform 10:537–546. https://doi.org/10.1093/bib/bbp016

    Article  Google Scholar 

  55. Papageorgiou EI, Poczęta K, Laspidou C (2015) Application of fuzzy cognitive maps to water demand prediction. In: 2015 IEEE international conference on fuzzy systems (FUZZ-IEEE). pp 1–8

  56. Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24:65–75. https://doi.org/10.1016/S0020-7373(86)80040-2

    Article  MATH  Google Scholar 

  57. Bertolini M (2007) Assessment of human reliability factors: a fuzzy cognitive maps approach. Int J Ind Ergon 37:405–413. https://doi.org/10.1016/j.ergon.2005.12.009

    Article  Google Scholar 

  58. Amirkhani A, Mosavi MR, Mohammadi K, Papageorgiou EI (2018) A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease. Neural Comput Appl 30:1573–1588. https://doi.org/10.1007/s00521-016-2765-y

    Article  Google Scholar 

  59. Jayashree LS, Palakkal N, Papageorgiou EI, Papageorgiou K (2015) Application of fuzzy cognitive maps in precision agriculture: a case study on coconut yield management of southern India’s Malabar region. Neural Comput Appl 26:1963–1978. https://doi.org/10.1007/s00521-015-1864-5

    Article  Google Scholar 

  60. Baykasoglu A (2012) Design optimization with chaos embedded great deluge algorithm. Appl Soft Comput 12:1055–1067. https://doi.org/10.1016/j.asoc.2011.11.018

    Article  Google Scholar 

  61. Baykasoglu A, Owen S, Gindy N (1999) A taboo search based approach to find the pareto optimal set in multiple objective optimization. Engineering Optimization 31:731–748. https://doi.org/10.1080/03052159908941394

    Article  Google Scholar 

  62. Kilic D, Ozturk S (2014) Gender differences in cigarette consumption in Turkey: evidence from the Global Adult Tobacco Survey. Health Policy 114:207–214. https://doi.org/10.1016/j.healthpol.2013.05.019

    Article  Google Scholar 

  63. İlhan MN, Arıkan Z, Kotan Z et al (2016) Prevalence and socio-demographic determinants of tobacco, alcohol, substance use and drug misuse in general population in Turkey. Noro Psikiyatr Ars 53:205–212. https://doi.org/10.5152/npa.2015.10050

    Article  Google Scholar 

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  1. Department of Industrial Engineering, Gaziantep University, 27310, Şehitkamil, Gaziantep, Turkey

    Pınar Kocabey Çiftçi & Zeynep Didem Unutmaz Durmuşoğlu

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  1. Pınar Kocabey Çiftçi
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  2. Zeynep Didem Unutmaz Durmuşoğlu
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All authors contributed to the presented study. They planned the study, decided to the methodologies, arranged the available data, performed the required analysis and wrote the article together in coordination.

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Correspondence to Pınar Kocabey Çiftçi.

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Kocabey Çiftçi, P., Unutmaz Durmuşoğlu, Z.D. A multi-stage learning-based fuzzy cognitive maps for tobacco use. Neural Comput & Applic 32, 15101–15118 (2020). https://doi.org/10.1007/s00521-020-04860-4

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