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Linguistic fuzzy consensus model for collaborative development of fuzzy cognitive maps: a case study in software development risks

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Abstract

Fuzzy cognitive maps (FCMs) have been widely used in several domains for information processing, such as, data fusion, decision making. Although several methods to automatically learn FCMs are recognized from the scientific literature, the most used approach to build an FCM relies on a collaborative task involving single person or, more suitably, group of experts. Collaborative development increases reliability and robustness of the resulting FCM, but rises some problems in terms of group decision making to aggregate different perspectives of the problem representation. This paper proposes to support collaborative development of FCMs introducing knowledge engineering process that relies on Linguistic Fuzzy Consensus Model. In the proposed approach, each expert builds the own version of the FCM. When all different versions are available, a Group Decision Making process is activated in order to reach the consensus on conflictual modeling opinions. The result is a unique final version of the FCM that is not a simple aggregation of the versions provided by the experts but is the result of a well-suited mathematical model. In addition, this work adopts consensus model with incomplete preference relations scheme to address knowledge harmonization issues. Finally, advantages and the limitations of the proposed framework are argued.

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Notes

  1. JFCM is a Java library for FCMs. http://jfcm.megadix.it/.

  2. Mental Modeler is modeling software for FCMs. http://www.mentalmodeler.org/.

  3. http://epapageorgiou.com/index.php/fcm-software-tools/39-fcm-wizard-software-tool.

References

  • Alonso, S., Cabrerizo, F. J., Chiclana, F., Herrera, F., & Herrera-Viedma, E. (2009). Group decision making with incomplete fuzzy linguistic preference relations. International Journal of Intelligent Systems, 24(2), 201–222.

    Article  MATH  Google Scholar 

  • Beena, P., & Ganguli, R. (2011). Structural damage detection using fuzzy cognitive maps and hebbian learning. Applied Soft Computing, 11(1), 1014–1020.

    Article  Google Scholar 

  • Bryson, N., Mobolurin, A., & Joseph, A. (1997). Generating consensus fuzzy cognitive maps. In Proceedings of intelligent information systems, 1997 (IIS’97) (pp. 231–235). IEEE.

  • Cabrerizo, F. J., Chiclana, F., Al-Hmouz, R., Morfeq, A., Balamash, A. S., & Herrera-Viedma, E. (2015a). Fuzzy decision making and consensus: Challenges. Journal of Intelligent & Fuzzy Systems, 29(3), 1109–1118.

    Article  MATH  MathSciNet  Google Scholar 

  • Cabrerizo, F. J., Morente-Molinera, J. A., Pérez, I. J., López-Gijón, J., & Herrera-Viedma, E. (2015b). A decision support system to develop a quality management in academic digital libraries. Information Sciences, 323, 48–58.

    Article  MathSciNet  Google Scholar 

  • Cabrerizo, F. J., Urea, R., Pedrycz, W., & Herrera-Viedma, E. (2014). Building consensus in group decision making with an allocation of information granularity. Fuzzy Sets and Systems, 255, 115–127. doi:10.1016/j.fss.2014.03.016.

    Article  MATH  MathSciNet  Google Scholar 

  • De Maio, C., Fenza, G., Gaeta, M., Loia, V., & Orciuoli, F. (2011). A knowledge-based framework for emergency dss. Knowledge-Based Systems, 24(8), 1372–1379.

    Article  Google Scholar 

  • Gray, S., Chan, A., Clark, D., & Jordan, R. (2012). Modeling the integration of stakeholder knowledge in social-ecological decision-making: Benefits and limitations to knowledge diversity. Ecological Modelling, 229, 88–96.

    Article  Google Scholar 

  • Herrera, F., Alonso, S., Chiclana, F., & Herrera-Viedma, E. (2009a). Computing with words in decision making: Foundations, trends and prospects. Fuzzy Optimization and Decision Making, 8(4), 337–364.

    Article  MATH  Google Scholar 

  • Herrera, F., Herrera-Viedma, E., Alonso, S., & Chiclana, F. (2009b). Computing with words and decision making. Fuzzy Optimization and Decision Making, 8(4), 323–324.

    Article  MATH  MathSciNet  Google Scholar 

  • Herrera, F., Herrera-Viedma, E., et al. (1996). A model of consensus in group decision making under linguistic assessments. Fuzzy Sets and Systems, 78(1), 73–87.

    Article  MathSciNet  Google Scholar 

  • Herrera, F., & Martínez, L. (2000). A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Transactions on Fuzzy Systems, 8(6), 746–752.

    Article  Google Scholar 

  • Herrera-Viedma, E., Herrera, F., & Chiclana, F. (2002). A consensus model for multiperson decision making with different preference structures. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 32(3), 394–402.

    Article  MATH  Google Scholar 

  • Kosko, B. (1992). Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence/book and disk (Vol. 1). Upper Saddle River: Prentice hall.

    MATH  Google Scholar 

  • Kumar, R., & Vassilvitskii, S. (2010). Generalized distances between rankings. In Proceedings of the 19th international conference on World wide web (pp. 571–580). ACM.

  • Morente-Molinera, J., Wikström, R., Herrera-Viedma, E., & Carlsson, C. (2016). An implementation of a linguistic mobile decision support system based on fuzzy ontologies to facilitate knowledge mobilization. Decision Support Systems, 81, 66–75.

    Article  Google Scholar 

  • Obiedat, M., & Samarasinghe, S. (2013). Fuzzy representation and aggregation of fuzzy cognitive maps. In The 20th International Congress on Modelling and Simulation (pp.  684–690). Modelling and Simulation Society of Australia and New Zealand.

  • Pérez, I. J., Cabrerizo, F. J., Alonso, S., & Herrera-Viedma, E. (2014). A new consensus model for group decision making problems with non-homogeneous experts. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 44(4), 494–498.

    Article  Google Scholar 

  • Pérez, I. J., Cabrerizo, F. J., & Herrera-Viedma, E. (2010). A mobile decision support system for dynamic group decision-making problems. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 40(6), 1244–1256.

    Article  Google Scholar 

  • Salmeron, J. L., Vidal, R., & Mena, A. (2012). Ranking fuzzy cognitive map based scenarios with topsis. Expert Systems with Applications, 39(3), 2443–2450.

    Article  Google Scholar 

  • Stach, W., Kurgan, L. A., & Pedrycz, W. (2005). Survey of fuzzy cognitive map learning methods. In P. Grzegorzewski, M. Krawczak, & S. Zadrozny (Eds.), Issues in Soft Computing: Theory and Applications (pp. 71–84). Berlin: Springer.

    Google Scholar 

  • Suárez-Figueroa, M. C., Gómez-Pérez, A., & Fernández-López, M. (2012). The NeOn methodology for ontology engineering. In M. C. Suárez-Figueroa, A. Gómez-Pérez, E. Motta, & A. Gangemi (Eds.), Ontology engineering in a networked world (pp. 9–34). Berlin: Springer. doi:10.1007/978-3-642-24794-1_2.

  • Subramanian, J., Karmegam, A., Papageorgiou, E., Papandrianos, N., & Vasukie, A. (2015). An integrated breast cancer risk assessment and management model based on fuzzy cognitive maps. Computer Methods and Programs in Biomedicine, 118(3), 280–297.

    Article  Google Scholar 

  • Yager, R. R., & Alajlan, N. (2014). On characterizing features of owa aggregation operators. Fuzzy Optimization and Decision Making, 13(1), 1–32.

    Article  MathSciNet  Google Scholar 

  • Yuen, K. K. F. (2014). Combining compound linguistic ordinal scale and cognitive pairwise comparison in the rectified fuzzy topsis method for group decision making. Fuzzy Optimization and Decision Making, 13(1), 105–130.

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, Elettrica e Matematica Applicata, University of Salerno, 84084, Fisciano, SA, Italy

    Carmen De Maio

  2. Department of Management and Innovation Systems, University of Salerno, 84084, Fisciano, SA, Italy

    Giuseppe Fenza, Vincenzo Loia & Francesco Orciuoli

Authors
  1. Carmen De Maio
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  2. Giuseppe Fenza
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  3. Vincenzo Loia
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  4. Francesco Orciuoli
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Correspondence to Carmen De Maio.

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De Maio, C., Fenza, G., Loia, V. et al. Linguistic fuzzy consensus model for collaborative development of fuzzy cognitive maps: a case study in software development risks. Fuzzy Optim Decis Making 16, 463–479 (2017). https://doi.org/10.1007/s10700-016-9259-3

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