A Novel Approach on Constructed Dynamic Fuzzy Cognitive Maps Using Fuzzified Decision Trees and Knowledge-Extraction Techniques

  • Elpiniki I. Papageorgiou
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 247)


A novel approach for the construction of augmented Fuzzy Cognitive Maps based on data mining and knowledge-extraction methods has been investigated for decision making and classification tasks. Specifically, through this work, the issue of designing decision support systems based on fuzzy cognitive maps has been explored using fuzzified decision trees and other knowledge-extraction techniques. Fuzzy cognitive map is a knowledge-based technique that works as an artificial cognitive network inheriting the main aspects of cognitive maps and artificial neural networks. Decision trees, in the other hand, are well known intelligent techniques that extract rules from both symbolic and numeric data. Fuzzy theoretical techniques are used to fuzzify crisp decision trees in order to soften decision boundaries at decision nodes inherent in this type of trees. Comparisons between crisp decision trees and the fuzzified decision trees suggest that the later fuzzy tree is significantly more robust and produces a more balanced decision making. The new approach proposed in this paper could incorporate any type of knowledge extraction algorithm. Furthermore, through the knowledge extraction methods the useful knowledge from data can be extracted in the form of fuzzy rules and inserted those into the FCM, contributing to the development of a dynamic approach for decision support. The proposed approach is implemented in a well known medical decision making problem to preview the effectiveness.


fuzzy cognitive maps decision trees fuzzy neuro-fuzzy data mining causal paths decision making 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. AAPM Report No. 55, Radiation Treatment planning dosimetry verification. American Association of Physicists in Medicine. Report of Task Group 23 of the Radiation Therapy Committee. American Institution of Physics, Woodbury (1995)Google Scholar
  2. Au, W.-H., Chan, K.C.C.: FARM: A data mining system for discovering fuzzy association rules. In: Proc. of the 8th IEEE International Conference on Fuzzy Systems, Seoul, Korea, August 22-25, pp. 1217–1222 (1999)Google Scholar
  3. Aguilar, J.: A survey about fuzzy cognitive maps papers. International Journal of Computational Cognition 3(2), 27–33 (2005)Google Scholar
  4. Alam, R., Ibbott, G.S., Pourang, R., Nath, R.: Application of AAPM Radiation Therapy Committee Task Group 23 test package for comparison of two treatment planning systems for photon external beam radiotherapy. Med. Phys. 24, 2043–2054 (1997)CrossRefGoogle Scholar
  5. Boutalis, Y., Kottas, T.L., Christodoulou, M.: Adaptive estimation of fuzzy cognitive maps with proven stability and parameter convergence. IEEE Transactions on Fuzzy Systems 17(4), 874–889 (2009)CrossRefGoogle Scholar
  6. Bueno, S., Salmeron, J.L.: Benchmarking main activation functions in fuzzy cognitive maps. Expert Systems with Applications 36(3), 5221–5229 (2009)CrossRefGoogle Scholar
  7. Chen, G., Wei, Q.: Fuzzy association rules and the extended mining algorithms. Information Sciences 147, 201–228 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  8. Crockett, K., Bandar, Z., Mclean, D., O’Shea, J.: On constructing a fuzzy inference framework using crisp decision trees. Fuzzy Sets and Systems 157, 2809–2832 (2006)zbMATHCrossRefMathSciNetGoogle Scholar
  9. Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Menlo Park (1996)Google Scholar
  10. Fayyad, U., Uthurusamy, R.: Data mining and knowledge discovery in databases. Commun. ACM 39, 24–27 (1996)CrossRefGoogle Scholar
  11. Froelich, W., Wakulicz-Deja, A.: Predictive Capabilities of Adaptive and Evolutionary Fuzzy Cognitive Maps - A Comparative Study. In: Nguyen, N.T., Szczerbicki, E. (eds.) Intel. Sys. for Know. Management. SCI, vol. 252, pp. 153–174. Springer, Berlin (2009)CrossRefGoogle Scholar
  12. Froelich, W., Wakulicz-Deja, A.: Mining temporal medical data using adaptive fuzzy cognitive maps. In: Proceedings - 2009 2nd Conference on Human System Interactions, HSI 2009, pp. 16–23 (2009) art. no. 5090946Google Scholar
  13. Fu, L.M.: Knowledge-Based Connectionism for Revising Domain Theories. IEEE Trans. on Systems, Man, and Cybernetics 23(l), 173–182 (1993)CrossRefGoogle Scholar
  14. Gath, G., Geva, A.B.: Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 7, 773–781 (1989)CrossRefGoogle Scholar
  15. Georgopoulos, V.C., Stylios, C.D.: Complementary case-based reasoning and competitive fuzzy cognitive maps for advanced medical decisions. Soft Computing 12, 191–199 (2008)CrossRefGoogle Scholar
  16. Georgopoulos, V.C., Stylios, C.D.: Augmented Fuzzy Cognitive Maps Supplemented with Case Base Reasoning for Advanced Medical Decision Support. In: Nikravesh, M., Zadeh, L.A., Kacprzyk, J. (eds.) Soft Computing for Information Processing and Analysis Enhancing the Power of the Information Technology. Studies in Fuzziness and Soft Computing, pp. 391–405. Springer, Heidelberg (2005) ISBN: 3-540-22930-2CrossRefGoogle Scholar
  17. Hayashi, Y., Maeda, T., Bastian, A., Jain, L.C.: Generation of fuzzy decision trees by fuzzy ID3 with adjusting mechanism of and/or operators. In: Proc. of Int. Conf. Fuzzy Syst., pp. 681–685 (1998)Google Scholar
  18. ICRU Report 50, Prescribing, recording and reporting photon beam therapy. International Commission on Radiation Units and Measurements, Washington (1993)Google Scholar
  19. Ishibuchi, H., Nozaki, K., Yamamoto, N., Tanaka, N.: Selecting fuzzy if–then rules for classification problems using genetic algorithms. IEEE Trans. Fuzzy Systems 3(3), 260–270 (1995)CrossRefGoogle Scholar
  20. Janikow, C.Z.: Fuzzy decision trees: issues and methods. IEEE Trans. Systems Man and Cybernetics 28(1), 1–14 (1998)Google Scholar
  21. Janikow, C.Z.: Fuzzy partitioning with FID3.1. In: Proceedings of the 18th International Conference of the North American Fuzzy Information Society, pp. 467–471 (1999)Google Scholar
  22. Janikow, C.Z.: Fuzzy Decision Trees Manual, free version for Fuzzy Decision Trees (1998)
  23. Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy & Soft Computing. Prentice-Hall, Upper Saddle River (1997)Google Scholar
  24. Jang, L.: Soft Computing Techniques in Knowledge-Based Intelligent Engineering Systems: Approaches and Applications. Studies in Fuzziness and Soft Computing, vol. 10. Springer, Heidelberg (1997)Google Scholar
  25. Khan, F.: The Physics of Radiation Therapy, 2nd edn. Williams & Wilkins, Baltimore (1994)Google Scholar
  26. Kosko, B.: Fuzzy Cognitive Maps. Int. J. Man-Machine Studies 24, 65–75 (1986)zbMATHCrossRefGoogle Scholar
  27. Kosko, B.: Neural Networks and Fuzzy Systems. Prentice-Hall, New Jersey (1992)zbMATHGoogle Scholar
  28. Kurgan, L.A., Musilek, P.: A Survey on Knowledge Discovery and Data mining processes. The Knowledge Engineering Review 21(1), 1–24 (2006)CrossRefGoogle Scholar
  29. Lee, K.C., Kim, H.S.: A Causal Knowledge-Driven Inference Engine for Expert System. In: Proc. of the 31st Hawaii International Conference on System Science, January 6-9, vol. 1(1), pp. 284–293 (1998)Google Scholar
  30. Liu, H., Tan, S.T.: X2R: A Fast Rule Generator. In: Proc of IEEE Inter. Conf. on Systems, Man & Cybernetics, Vancouver, Canada (October 1995)Google Scholar
  31. Liu, X., Cohen, P., Berthold, M.R.: IDA 1997. LNCS, vol. 1280. Springer, Heidelberg (1997)Google Scholar
  32. Lozowski, A., Zurada, J.M.: Extraction of linguistic rules from data via neural networks and fuzzy approximation. In: Cloete, J., Zurada, J.M. (eds.) Knowledge-Based Neurocomputing. The MIT Press, Cambridge (2000)Google Scholar
  33. Miao, Y., Liu, Z.Q.: On causal inference in fuzzy cognitive maps. IEEE Transactions on Fuzzy Systems 8, 107–119 (2000)CrossRefGoogle Scholar
  34. Mitra, S., Konwar, K.M., Sankar, K.P.: Fuzzy decision tree, linguistic rules and fuzzy knowledge-based network: generation and evaluation. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 32(4), 328–339 (2002)CrossRefGoogle Scholar
  35. Mitra, S., Hayashi, Y.: Neuro-Fuzzy rule generation: Survey in soft computing. IEEE Trans Neural Networks 11(3), 748–760 (2000)CrossRefGoogle Scholar
  36. Nauck, D., Klawonn, F., Kruse, R.: Foundations of neuro-fuzzy systems. Wiley, Chichester (1997)Google Scholar
  37. Nauck, D., Kruse, R.: Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicin 16(2), 149–169 (1999)CrossRefMathSciNetGoogle Scholar
  38. Nauck, D.: NEFCLASS toolbox (1997),
  39. Olaru, C.W.: A complete fuzzy decision tree technique. Fuzzy Sets and Systems 138, 221–254 (2003)CrossRefMathSciNetGoogle Scholar
  40. Pach, F.P., Abonyi, J.: Association Rule and Decision Tree based Methods for Fuzzy Rule Base Generation. Transactions on Engineering, Computing and Technology 13 (2006) ISSN 1305-5313Google Scholar
  41. Pal, S.K., Mitra, S.: Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. Wiley, New York (1999)Google Scholar
  42. Papageorgiou, E., Stylios, C., Groumpos, P.: An Integrated Two-Level Hierarchical Decision Making System based on Fuzzy Cognitive Maps (FCMs). IEEE Trans. Biomed. Engin. 50(12), 1326–1339 (2003)CrossRefGoogle Scholar
  43. Papageorgiou, E.I.: A model for dose calculation in treatment planning using pencil beam kernels. MSc. Thesis, Medical University Hospital of Patras, Greece (June 2000)Google Scholar
  44. Papageorgiou, E.I., Groumpos, P.P.: A weight adaptation method for fine-tuning Fuzzy Cognitive Map causal links. Soft Computing 9, 846–857 (2005a)zbMATHCrossRefGoogle Scholar
  45. Papageorgiou, E.I., Groumpos, P.P.: A new hybrid learning algorithm for Fuzzy Cognitive Maps learning. Applied Soft Computing 5, 409–431 (2005b)CrossRefGoogle Scholar
  46. Papageorgiou, E.I., Spyridonos, P., Ravazoula, P., Stylios, C.D., Groumpos, P.P., Nikiforidis, G.: Advanced Soft Computing Diagnosis Method for Tumor Grading. Artificial Intelligence in Medicine 36(1), 59–70 (2006a)CrossRefGoogle Scholar
  47. Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: A Combined Fuzzy Cognitive Map and Decision Trees Model for Medical Decision Making. In: Proceedings of the 28th IEEE EMBS Annual Intern. Conference in Medicine and Biology Society, EMBS 2006, New York, USA, 30 August-3 September, pp. 6117–6120 (2006b)Google Scholar
  48. Papageorgiou, E.I., Groumpos, P.P.: Neuro-fuzzy, fuzzy decision tree and association rule based methods for fuzzy cognitive map grading process. In: Proceedings of International Conference on Computational Intelligence in MEDicine, CIMED 2007, Plymouth, UK, July 25-27 (2007) (CD-ROM)Google Scholar
  49. Papageorgiou, E.I., Spyridonos, P., Glotsos, D., Stylios, C.D., Ravazoula, P., Nikiforidis, G., Groumpos, P.P.: Brain tumour characterization using the soft computing technique of fuzzy cognitive maps. Applied Soft Computing 8, 820–828 (2008)CrossRefGoogle Scholar
  50. Papageorgiou, E.I., Papandrianos, N., Apostolopoulos, D., Vassilakos, P.: Fuzzy Cognitive Map based Decision Support System for thyroid diagnosis management. In: Zurada, J.M., Yen, G.G., Wang, J. (eds.) Computational Intelligence: Research Frontiers. LNCS, vol. 5050, pp. 1204–1211. Springer, Heidelberg (2008)Google Scholar
  51. Papakostas, G.A., Boutalis, Y.S., Koulouriotis, D.E., Mertzios, B.G.: Fuzzy cognitive maps for pattern recognition applications. International Journal of Pattern Recognition and Artificial Intelligence 22(8), 1461–1486 (2008)CrossRefGoogle Scholar
  52. Pedrycz, W., Sosnowski, A.: Designing decision trees with the use of fuzzy granulation. IEEE Trans. Syst. Man Cybern. A 30, 151–159 (2000)CrossRefGoogle Scholar
  53. Peláez, C.E., Bowles, J.B.: Using fuzzy cognitive maps as a system model for failure modes and effects analysis. Information Sciences 88, 177–199 (1996)CrossRefGoogle Scholar
  54. Quinlan, J.R.: Decision trees and decision making. IEEE Trans System, Man and Cybernetics 20(2), 339–346 (1990)CrossRefGoogle Scholar
  55. Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  56. Quinlan, J.R.: Is C5.0 better than C4.5 (2002),
  57. Sestino, S., Dillon, T.: Using single-layered neural networks for the extraction of conjunctive rules and hierarchical classifications. J. Appl. Intell. 1, 157–173 (1991)CrossRefGoogle Scholar
  58. Sison, L., Chong, E.: Fuzzy modeling by induction and pruning of decision trees. In: IEEE Symposium on Intelligent Control, U.S.A., pp. 166–171 (1994)Google Scholar
  59. Sordo, M., Vaidya, S., Jain, L.C.: An introduction to computational intelligence in healthcare: New directions. Studies in Computational Intelligence 107, 1–26 (2008)CrossRefGoogle Scholar
  60. Stach, W., Kurgan, L., Petrycz, W.: A Framework for a novel scalable FCM learning method. In: Proceedings of the 2007 Symposium on Human-Centric Computing and Data Processing (HCDP 2007), Canada, February 21 - 23, pp. 13–14 (2007)Google Scholar
  61. Stylios, C.D., Georgopoulos, V.C., Malandraki, G.A., Chouliara, S.: Fuzzy cognitive map architectures for medical decision support systems. Appl. Soft Comput. 8(3), 1243–1251 (2008)CrossRefGoogle Scholar
  62. Stylios, C.D., Groumpos, P.P.: Modeling Fuzzy Cognitive Maps. IEEE Transactions on Systems, Man, and Cybernetics, Part A 34, 155–162 (2004)CrossRefGoogle Scholar
  63. Taber, R., Yager, R., Helgason, C.M.: Quantization Effects on the Equilibrium Behavior of Combined Fuzzy Cognitive Maps. International Journal of Intelligent Systems 22, 181–202 (2007)zbMATHCrossRefGoogle Scholar
  64. Towell, G., Shavlik, J.: Extracting Refined Rules from Knowledge-Based Neural Networks. Machine Learning 131, 71–101 (1993)Google Scholar
  65. Umano, M., Okamoto, H., Hatono, I., Tamura, H.: Generation of fuzzy decision trees by fuzzy ID3 algorithm and its application to diagnosis by gas in oil. In: Japan–U.S.A. Symposium, pp. 1445–1450 (1994)Google Scholar
  66. Weber, R.: Fuzzy ID3: a class of methods for automatic knowledge acquisition. In: 2nd International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, pp. 265–268 (1992)Google Scholar
  67. Wei, Z., Baowen, S., Yanchun, Z.: Design of inference model based on activation for fuzzy cognitive map. In: 2009 International Workshop on Intelligent Systems and Applications, ISA 2009 (2009) art. no. 5072819Google Scholar
  68. Wells, D., Niederer, J.: A Medical Expert System approach using Artificial Neural Networks for standardized treatment planning. Int. J. Radiat. Oncol. Biol. Phys. 41(1), 173–182 (1998)Google Scholar
  69. Xirogiannis, G., Chytas, P., Glykas, M., Valiris, G.: Intelligent impact assessment of HRM to the shareholder value. Expert Systems with Applications 35(4), 2017–2031 (2008)CrossRefGoogle Scholar
  70. Xirogiannis, G., Stefanou, J., Glykas, M.: A fuzzy cognitive map approach to support urban design. Expert Systems with Applications 26(2), 257–268 (2004)CrossRefGoogle Scholar
  71. Xirogiannis, G., Glykas, M.: Intelligent Modeling of e-Business Maturity. Expert Systems with Applications 32/2, 687–702 (2007)CrossRefGoogle Scholar
  72. Yuan, Y., Shaw, M.J.: Induction of fuzzy decision trees. Fuzzy Sets Systems 69, 125–139 (1995)CrossRefMathSciNetGoogle Scholar
  73. Zurada, J.M., Duch, W., Setiono, R.: Computational intelligence methods for rule-based data understanding. In: Proc. of the IEEE International Conference on Neural Networks, vol. 92(5), pp. 771–805 (2004)Google Scholar
  74. Zurada, J.M., Lozowski, A.: Generating linguistic rules from data using neuro-fuzzy framework. In: Proc. 4th Intern. Conf. on Soft. Computing (IIZUKA 1996), Iizuka, Fukuoda, Japan, pp. 618–621 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Elpiniki I. Papageorgiou
    • 1
  1. 1.Dept of Informatics and Computer TechnologyTechnological Educational Institute of Lamia, TEI LamiasLamiaGreece

Personalised recommendations