Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/32397
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dc.contributor.authorDIKOPOULOU, Zoumpolia-
dc.contributor.authorPAPAGEORGIOU, Elpiniki-
dc.contributor.authorVANHOOF, Koen-
dc.date.accessioned2020-10-01T13:24:50Z-
dc.date.available2020-10-01T13:24:50Z-
dc.date.issued2020-
dc.date.submitted2020-09-30T19:13:39Z-
dc.identifier.citation2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, p. 1 -8-
dc.identifier.isbn9781728169323-
dc.identifier.issn1558-4739-
dc.identifier.urihttp://hdl.handle.net/1942/32397-
dc.description.abstractFuzzy cognitive maps (FCMs) have gained popularity within the scientific community due to their capabilities in modelling and decision making for complex problems. However, learning FCM models automatically from data without any expert knowledge and/or historical data remains a considerable challenge. For our research, we use the estimated weight matrix from the graphical lasso (glasso) method with the EBIC regulation technique. Particularly, the glasso is a technique originated from machine learning which is used to model a problem by learning the weight matrix directly from a dataset. Moreover, the relationships are expressed by conditional independence among two nodes after conditioning on all the other nodes of the graph. However, the challenging task in this study is the investigation of the suitable transformation of the weight matrix from a symmetric matrix to asymmetric in order to determine the directions of the edges among the concepts and construct the glassoFCM model. For this reason, statistical comparisons are applied to examine if there are significant differences in the value of the output concept when the input concepts are rearranged according to four different cases. The whole approach was implemented in a business intelligence problem of evaluating the willingness of the employees to work in Belgian companies.-
dc.language.isoen-
dc.publisherIEEE-
dc.subject.otherfuzzy cognitive map-
dc.subject.otherglassoFCM method-
dc.subject.othergraphical lasso models-
dc.subject.otherdata-driven approach-
dc.subject.otherordinal data-
dc.titleFrom Undirected Structures to Directed Graphical Lasso Fuzzy Cognitive Maps using Ranking-based Approaches-
dc.typeProceedings Paper-
local.bibliographicCitation.conferencedate19-24 July 2020-
local.bibliographicCitation.conferencename2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)-
local.bibliographicCitation.conferenceplaceGlasgow, United Kingdom-
dc.identifier.epage8-
dc.identifier.spage1-
local.format.pages8-
local.bibliographicCitation.jcatC1-
local.publisher.place345 E 47TH ST, NEW YORK, NY 10017 USA-
dc.relation.references[1] B. Kosko, “Fuzzy cognitive maps”, International Journal of Man-Machine Studies, 24 (1), pp. 65–75, 1986. [2] B. Kosko, “Neural Networks and Fuzzy Systems: A Dynamical Systems Approach to Machine Intelligence”, Prentice-Hall. New York, 1992. [3] E. Papageorgiou, J. L. Salmeron. “Methods and Algorithms for Fuzzy Cognitive Map-based Modeling”, Intelligent Systems Reference Library 54, pp. 1-28, 2014. [4] M. Schneider, E. Shnaider, A. Kandel and G. Chew, “Automatic construction of FCMs”, Fuzzy Sets and Systems, 93 (2), pp. 161–172, 1998. [5] E. I. Papageorgiou, A. Kontogianni, “Using fuzzy cognitive mapping in environmental decision making and management: a methodological primer and an application” in S. S. Young and S. E. Silvern, editors. International perspectives on global environmental change. InTech, Rijeka, Croatia, pp. 427-450, 2012. [6] R. Tibshirani, “Regression shrinkage and selection via the lasso”, Journal of the Royal statistical society, B 58, pp. 1436–1462, 2006. [7] B. W. Brunton, and M. Beyeler, “Data-driven models in human neuroscience and neuroengineering”, Current Opinion in Neurobiology, 58, 21–29, 2019. [8] M. E. J. Newman, “Networks: an introduction”; Oxford, UK: Oxford University Press, 2010. [9] J. H. Friedman, T. Hastie, and R. Tibshirani, “ Sparse inverse covariance estimation with the graphical lasso”, Biostatistics, 9(3):432–441, 2008. [10] G. Xirogiannis and M. Glykas, “Fuzzy Cognitive Maps in Business Analysis and Performance-Driven Change”, IEEE Transactions on Engineering Management, 51(3), 334–351, 2004. [11] J. P. Craiger, D.F. Goodman, R. J. Weiss, & A. Butler, “Modeling organizational behavior with Fuzzy Cognitive Maps”, Int. J. Comput. Intell. Org., vol. 1, pp. 120–123, 1996. [12] Z. Dikopoulou, E. Papageorgiou, V. Mago, K. Vanhoof, “A new approach using Mixed Graphical Model for automated design of Fuzzy Cognitive Maps from ordinal data”, IEEE International Conference on Fuzzy Systems, Naples, Italy, July 9-12, 2017. [13] Z. Dikopoulou, E. Papageorgiou, K. Vanhoof, “Retrieving sparser Fuzzy Cognitive Maps directly from large ordinal dataset using lasso graphical models and the max-threshold algorithm”, IEEE world congress on computational intelligence (WCCI), 19 - 24th July, 2020, Glasgow (UK), 2020. (accepted) [14] P. Zhao, and B. Yu, “On model selection consistency of lasso”, The Journal of Machine Learning Research, 7:2541–2563, 2006. [15] J. Chen and Z. Chen, “Extended bayesian information criteria for model selection with large model spaces”, Biometrika, 95 (3), 759–771, 2008. [16] R. Foygel, M. Drton, “Bayesian model choice and information criteria in sparse generalized linear models”, 2011. arXiv preprint arXiv:1112.5635. [17] J. H. Friedman, T. Hastie, and R. Tibshirani “ glasso: Graphical lasso estimation of gaussian graphical models”, 2014. [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=glasso [18] S. Epskamp, A. Cramer, L. Waldorp, V. D. Schmittmann, and D. Borsboom, “qgraph: Network visualizations of relationships in psychometric data”, Journal of Statistical Software, 48 (4), 1-18, 2012. [19] H. M. Weiss, and K. L. Merlo, “Job Satisfaction”, International Encyclopedia of the Social & Behavioral Sciences, 833–838, 2015. [20] E.I. Papageorgiou, “Fuzzy Cognitive Maps for Applied Sciences and Engineering From Fundamentals to Extensions and Learning Algorithms”, Intelligent Systems Reference Library, Vol. 54, 2014. [21] M. Bougon, K. Weick, and D. Binkhorst, “Cognition in Organizations: An Analysis of the Utrecht Jazz Orchestra”, Administrative Science Quarterly, 22(4), 606, 1977. [22] S. S. Shapiro and M. B. Wilk, “An analysis of variance test for normality (complete samples)”, Biometrika, 52 (3-4), pp. 591–611, 1965. [23] R. A. Fisher, “The Correlation Between Relatives on the Supposition of Mendelian Inheritance”, Philosophical Transactions of the Royal Society of Edinburgh, 52, 399–433, 1918. [24] Z. Dikopoulou and E. Papageorgiou, “Inference of Fuzzy Cognitive Maps (FCMs)”, 2017. https://cran.r-project.org/web/packages/fcm/vignettes/ vignettes.html. [25] W. Stach, W. Pedrycz, L. A. Kurgan, “Learning of fuzzy cognitive maps using density estimate”, IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42 (3) (2012) 900–912. [26] A. Amirkhani, EI. Papageorgiou, A. Mohseni, MR. Mosavi, “A review of fuzzy cognitive maps in medicine: Taxonomy, methods, and applications”, Computer Methods and Programs in Biomedicine 142, (2017) 129-145. [27] P. Muenning, “Decision Analytic Modeling”, International Encyclopedia of Public Health, 71–76, 2008. [28] V.C.P. Knowland, H. Purser, M.S.C. Thomas, “Cross-Sectional Methodologies in Developmental Psychology”, International Encyclopedia of the Social & Behavioral Sciences, pp. 354-360, 2015. [29] S. Ahmadi, E. Papageorgiou, C.-H. Yeh, and R. Martin, “Managing readiness-relevant activities for the organizational dimension of ERP implementation”, Computers in Industry, 68, pp. 89–104, 2015. [30] M. Arvan, A. Omidvar, and R. Ghodsi, “Intellectual capital evaluation using fuzzy cognitive maps: A scenario-based development planning”, Expert Systems with Applications, 55, pp. 21–36, 2016. [31] M. Zarrin and A. Azadeh, “Mapping the influences of resilience engineering on health, safety, and environment and ergonomics management system by using Z -number cognitive map”, Human Factors and Ergonomics in Manufacturing & Service Industries, 2018. [32] E. Bakhtavar and Y. Shirvand, “Designing a fuzzy cognitive map to evaluate drilling and blasting problems of the tunneling projects in Iran”, Engineering with Computers, 2018. [33] C. D. Stylios, E. Bourgani and V. C. Georgopoulos, “Impact and Applications of Fuzzy Cognitive Map”, 2020, pp. 229-246. Methodologies Kosheleva, O., Shary, S. P., Xiang, G., & Zapatrin, R. (Eds.). (2020). Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications. Studies in Computational Intelligence. [34] P. Zhang, “Model selection via multifold cv”, Ann. Statist., 21, (1993), 299-311. [35] S. L. Lauritzen. “Graphical Models”, Springer Verlag, 1996. [36] J. M. B. Haslbeck & , L. J Waldorp “mgm: Structure estimation for time-varying mixed graphical models in high-dimensional Data”, 2016. [37] R. Mazumder, and T. Hastie, “The graphical lasso: New insights and alternatives”, Electron. J. Statist, 6, (2012), 2125-2149. [38] S. Epskamp, “Regularized Gaussian Psychological Networks: Brief Report on the Performance of Extended BIC Model Selection”, 2016. [39] S. Epskamp and E. I., Fried, “A Tutorial on Regularized Partial Correlation Networks”, Psychological Methods, 2007. [40] K. Wu, J. Liu, Robust learning of large-scale fuzzy cognitive maps via the lasso from noisy time series, Knowl.-Based Syst. 113 (2016) 23–38. [41] K. Wu and J. Liu, “Learning of sparse fuzzy cognitive maps using evolutionary algorithm with lasso initialization,” in Proc. Asia–Pac. Conf. Simulat. Evol. Learn., Shenzhen, China, Nov. 2017, pp. 385–396. [42] Y. Chi and J. Liu, "Learning of Fuzzy Cognitive Maps With Varying Densities Using A Multiobjective Evolutionary Algorithm," in IEEE Transactions on Fuzzy Systems, vol. 24, no. 1, pp. 71-81, Feb. 2016. [43] F. Shen, J. Liu, K. Wu, “Evolutionary multitasking fuzzy cognitive map learning”, Knowledge-Based Systems, 192, 2020. [44] E. Harary, F., Norman, R.Z., Cartwright, D., “Structural Models: An Introduction to the Theory of Directed Graphs”, John Wiley & Sons, New York, 1965. [45] M. Bougon, K. Weick and D. Binkhorst, “Cognition in organizations: an analysis of the Utrecht Jazz Orchestra”, Admin. Sci. Quart. 22, pp. 606–639, 1977. [46] S. Epskamp, D. Borsboom, E.I. Fried, “Estimating Psychological Networks and their Accuracy: A Tutorial Paper.” Behavior Research Methods, 2017. [47] G. R. Grimmett, “A theorem about random fields”, Bull. Lond. Math. Soc. 5, pp. 81–84, 1973. [48] M. Kiani, J. Andreu-Perez, H. Hagras, E. I. Papageorgiou, M. Prasad and C. Lin, "Effective Brain Connectivity for fNIRS with Fuzzy Cognitive Maps in Neuroergonomics," in IEEE Transactions on Cognitive and Developmental Systems, 2019.-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
dc.identifier.doi10.1109/FUZZ48607.2020.9177724-
dc.identifier.isi000698733400162-
local.provider.typeCrossRef-
local.bibliographicCitation.btitle2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)-
local.uhasselt.uhpubyes-
item.contributorDIKOPOULOU, Zoumpolia-
item.contributorPAPAGEORGIOU, Elpiniki-
item.contributorVANHOOF, Koen-
item.fulltextWith Fulltext-
item.validationecoom 2022-
item.validationvabb 2022-
item.fullcitationDIKOPOULOU, Zoumpolia; PAPAGEORGIOU, Elpiniki & VANHOOF, Koen (2020) From Undirected Structures to Directed Graphical Lasso Fuzzy Cognitive Maps using Ranking-based Approaches. In: 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, p. 1 -8.-
item.accessRightsRestricted Access-
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