Interpretation of results of intelligent analysis of fuzzy knowledge bases

Authors

DOI:

https://doi.org/10.15587/1729-4061.2014.24364

Keywords:

fuzzy knowledge base, intelligent data analysis, interpretation of analysis results

Abstract

One of the most important factors when considering the results, obtained by the intelligent analysis of fuzzy knowledge bases is the readability, comprehensibility, clarity and transparency of the results. All of them can be considered as some aspects of interpretation. It is not obvious today which constraints should be selected or imposed in order to be able to adequately and correctly interpret the output results of the analysis when developing and implementing the extraction methods for fuzzy knowledge bases. The paper describes these constraints and provides a list of its own (for fuzzy sets, membership functions, linguistic variables and fuzzy production rules), which can be used in practice to improve the interpretation of the analysis results and maintain the content of the fuzzy knowledge base at the appropriate level of quality during the extraction process. The feasibility of incorporating each of the proposed constraints is determined according to the specifics of the extraction problem. In practice, it is recommended that as many as possible compatible constraints be used and maintained, as this will greatly simplify the interpretation process by man.

Author Biographies

Олександр Юхимович Сєдушев, Lviv Polytechnic National University Bandera 12, Lviv, Ukraine, 79013

Ph. D. Student

Department of Information Systems and Networks

Євген Вікторович Буров, Lviv Polytechnic National University Bandera 12, Lviv, Ukraine, 79013

Ph. D., Assoc. Professor

Department of Information Systems and Networks

References

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  13. Mencar, C., Castellano, G., Fanelli, A. (2005). Some fundamental interpretability issues in fuzzy modeling. The European Society for Fuzzy Logic and Technology, 100–105.
  14. Van de Merckt, T., Decaestecker, C. (1995). Multiple knowledge representations in concept learning. Lecture Notes in Computer Science, 912, 200–217.
  15. Stepnickova, L., Stepnicka, M., Dvorak, A. (2013). New results on redundancies of fuzzy/linguistic if-then rules. The European Society for Fuzzy Logic and Technology, 400–407.
  16. Chen, M., Linkens, D. (2004). Rule-base self-generation and simplification for data-driven fuzzy models. Fuzzy Sets and Systems, 142, 243–265.
  17. Carpena, G., Ruiz, J., Munoz, J., Jimenez, F. (2008). Improving interpretability of fuzzy models using multi-objective neuro-evolutionary algorithms. Advances in Evolutionary Algorithms, 279–296.
  18. Casillas, J., Cordon, O., Herrera, F., Magdalena, L. (2003). Interpretability improvements to find the balance interpretability–accuracy in fuzzy modeling: An overview. Studies in Fuzziness and Soft Computing, 128, 3–22.
  19. Mencar, C., Castellano, G., Fanelli, A. (2007). On the role of interpretability in fuzzy data mining. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15, 521–537.
  20. Alonso, J., Magdalena, L. (2009). A Conceptual framework for understanding a fuzzy system. The European Society for Fuzzy Logic and Technology, 119–124.
  21. Zhou, S. M., Gan, J. (2008). Low-level interpretability and high-level interpretability: A unified view of data-driven interpretable fuzzy system modelling. Fuzzy Sets and Systems, 159, 3091–3131.
  22. Mencar, C., Fanelli, A. (2008). Interpretability constraints for fuzzy information granulation. Information Sciences, 178, 4585–4618.

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Published

2014-06-25

How to Cite

Сєдушев, О. Ю., & Буров, Є. В. (2014). Interpretation of results of intelligent analysis of fuzzy knowledge bases. Eastern-European Journal of Enterprise Technologies, 3(2(69), 30–35. https://doi.org/10.15587/1729-4061.2014.24364

Issue

Vol. 3 No. 2(69) (2014): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2014 Олександр Юхимович Сєдушев, Євген Вікторович Буров

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