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International Workshop on Artificial Intelligence and Pattern Recognition

IWAIPR 2021: Progress in Artificial Intelligence and Pattern Recognition pp 249–259Cite as

Data Drive Fuzzy Cognitive Map for Classification Problems

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13055))

Abstract

In recent years Fuzzy Cognitive Maps had become an important tool for expert knowledge representation due to the flexibility and interpretability of modeled maps. Its construction frequently requires an expert’s intervention but, there are situations when only the data is available or is required to extract the contained implicit knowledge for analysis or decision making proposes. Several studies have been developed to improve or find causal relation values between the map concepts but usually require a previous concept definition step carried out by experts. The frequent pattern mining techniques show a way for non-trivial relations extraction from datasets, and those relations may represent a causality degree. In this paper, a strategy to extract concepts from continuous and discrete features for supervised classification problems is proposed. Additionally, to estimate the causality degree between defined map concepts is proposed to use association rule mining techniques. Finally, the strategy is evaluated to show the interpretability and accuracy of generated Fuzzy Cognitive Maps.

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References

  1. Aggarwal, C.C., Bhuiyan, M.A., Al Hasan, M.: Frequent pattern mining algorithms: a survey. In: Aggarwal, C., Han, J. (eds.) Frequent Pattern Mining, pp. 19–64. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07821-2_2

  2. Amirkhani, A., Mosavi, M.R., Mohammadi, K., Papageorgiou, E.I.: A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease. Neural Comput. Appl. 30(5), 1573–1588 (2018)

    Google Scholar 

  3. Amirkhani, A., Papageorgiou, E.I., Mohseni, A., Mosavi, M.R.: A review of fuzzy cognitive maps in medicine: taxonomy, methods, and applications. Comput. Methods Programs Biomed. 142, 129–145 (2017)

    Google Scholar 

  4. Bezdek, J.C.: Fuzzy-Mathematics in Pattern Classification. Cornell University, Ithaca (1973)

    Google Scholar 

  5. Casillas, J., Cordón, O., Triguero, F.H., Magdalena, L.: Interpretability Issues in Fuzzy Modeling, vol. 128. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-540-37057-4

  6. Cohen, J., Cohen, P., West, S.G., Aiken, L.S.: Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Routledge, Abingdon (2013)

    Google Scholar 

  7. Felix, G., Nápoles, G., Falcon, R., Froelich, W., Vanhoof, K., Bello, R.: A review on methods and software for fuzzy cognitive maps. Artif. Intell. Rev. 52(3), 1707–1737 (2019)

    Google Scholar 

  8. Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D.: Do we need hundreds of classifiers to solve real world classification problems? J. Mach. Learn. Res. 15(1), 3133–3181 (2014)

    Google Scholar 

  9. He, H., Tan, Y., Fujimoto, K.: Estimation of optimal cluster number for fuzzy clustering with combined fuzzy entropy index. In: 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 697–703. IEEE (2016)

    Google Scholar 

  10. Kim, J., Han, M., Lee, Y., Park, Y.: Futuristic data-driven scenario building: incorporating text mining and fuzzy association rule mining into fuzzy cognitive map. Expert Syst. Appl. 57, 311–323 (2016)

    Google Scholar 

  11. Kosko, B.: Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 24(1), 65–75 (1986)

    Google Scholar 

  12. Kottas, F., Boutalis, Y.S., Devedzic, G., Mertzios, B.G.: A new method for reaching equilibrium points in fuzzy cognitive maps. In: 2004 2nd International IEEE Conference on ‘Intelligent Systems’. Proceedings (IEEE Cat. No. 04EX791), vol. 1, pp. 53–60. IEEE (2004)

    Google Scholar 

  13. Li, J., Liu, L., Le, T.D.: Practical Approaches to Causal Relationship Exploration. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-14433-7

  14. Nápoles, G., Leon Espinosa, M., Grau, I., Vanhoof, K., Bello, R.: Fuzzy cognitive maps based models for pattern classification: advances and challenges. In: Pelta, D., Cruz Corona, C. (eds.) Soft Computing Based Optimization and Decision Models, vol. 360, pp. 83–98. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-64286-4_5

  15. Nápoles, G., Jastrzebska, A., Mosquera, C., Vanhoof, K., Homenda, W.: Deterministic learning of hybrid fuzzy cognitive maps and network reduction approaches. Neural Netw. 124, 258–268 (2020)

    Google Scholar 

  16. Oostwal, E., Straat, M., Biehl, M.: Hidden unit specialization in layered neural networks: ReLU vs. sigmoidal activation. Phys. A: Stat. Mech. Appl. 564, 125517 (2021)

    Google Scholar 

  17. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    Google Scholar 

  18. Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25(1–2), 161–197 (1998)

    Google Scholar 

  19. Stylios, C.D., Bourgani, E., Georgopoulos, V.C.: Impact and applications of fuzzy cognitive map methodologies. In: Kosheleva, O., Shary, S., Xiang, G., Zapatrin, R. (eds.) Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy etc. Methods and Their Applications. SCI, vol. 835, pp. 229–246. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-31041-7_13

  20. Yuan, K., Liu, J., Yang, S., Wu, K., Shen, F.: Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps. Knowl.-Based Syst. 206, 106359 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Universidad Central “Marta Abreu” de Las Villas, Villa Clara, Cuba

    Jairo A. Lefebre-Lobaina & María M. García

Authors
  1. Jairo A. Lefebre-Lobaina
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  2. María M. García
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Corresponding authors

Correspondence to Jairo A. Lefebre-Lobaina or María M. García .

Editor information

Editors and Affiliations

  1. Universidad de las Ciencias Informáticas, La Habana, Cuba

    Yanio Hernández Heredia

  2. Universidad de las Ciencias Informáticas, La Habana, Cuba

    Vladimir Milián Núñez

  3. Universidad de las Ciencias Informáticas, La Habana, Cuba

    José Ruiz Shulcloper

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Lefebre-Lobaina, J.A., García, M.M. (2021). Data Drive Fuzzy Cognitive Map for Classification Problems. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2021. Lecture Notes in Computer Science(), vol 13055. Springer, Cham. https://doi.org/10.1007/978-3-030-89691-1_25

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  • DOI: https://doi.org/10.1007/978-3-030-89691-1_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89690-4

  • Online ISBN: 978-3-030-89691-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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