Vestnik KRAUNC. Fiz.-Mat. Nauki. 2022. vol. 40. no. 3. pp. 199–210. ISSN 2079-6641

Contents of this issue

Read Russian Version US Flag

MSC 68T27

Research Article

Using multivalued logic for qualitative data analysis

L. A. Lyutikova

Institute of Applied Mathematics and Automation KBSC RAS, 360000, Nalchik, Shortanova st., 89a, Russia
E-mail: lylarisa@yandex.ru

The article considers a logical approach to data analysis for solving the classification problem. The studied data is a set of objects and their features. As a rule, this is disparate heterogeneous information, and it is not enough for a reasonable application of probabilistic models. Therefore, logical algorithms are considered, which, under certain conditions, may be more adequate. For an expressive formal representation of the relationship between objects and their attributes, multivalued logic is used, and the number of values depends on the particular attribute. Therefore, a system of operations on variables with different domains is proposed. As a result, a decision function is built, which is a classifier of objects present in the studied data. The properties and possibilities of this function are analyzed. It is shown that a logical function, which is a conjunction in the space of rules that connect given objects with their characteristic features, unambiguously characterizes the initial data, divides the subject area into classes, has modifiability properties, satisfies the requirements of completeness and consistency in the given area. The paper also proposes an algorithm for its implementation.

Key words: intellectual system, predicate logic, predicate, decision function, class, subject area.

DOI: 10.26117/2079-6641-2022-40-3-199-210

Original article submitted: 30.10.2022

Revision submitted: 10.11.2022

For citation. Lyutikova L. A. Using multivalued logic for qualitative data analysis. Vestnik KRAUNC. Fiz.-mat. nauki. 2022, 40: 3, 199-210. DOI: 10.26117/2079-6641-2022-40-3-199-210

Competing interests. The authors declare that there are no conflicts of interest regarding authorship and publication.

Contribution and Responsibility. All authors contributed to this article. Authors are solely responsible for providing the final version of the article in print. The final version of the manuscript was approved by all authors.

The content is published under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/deed.ru)

© Lyutikova L. A., 2022

References

  1. Zhuravljov Ju. I. Ob algebraicheskom podhode k resheniju zadach raspoznavanija ili klassifikacii [On an algebraic approach to solving problems of recognition or classification]. Problemy kibernetiki, 1978, 33, 5-68. (In Russian).
  2. Shibzukhov Z. M. Correct algorithms for aggregating operations. Raspoznavanie obrazov i analiz izobrazhenij, 2014, 24:3, 377-382 (In Russian).
  3. Naimi A. I., Balzer L. B. Multilevel generalization: an introduction to super learning, European Journal of Epidemiology, 2018, 33, 459–464.
  4. Haoxiang W., Smith S. Big data analysis and perturbation using a data mining algorithm, Journal of Soft Computing Paradigm (JSCP), 2021. 3(01), 19-28.
  5. Joe M. C. V., Raj J. S. Location-based orientation context dependent recommender system for users, Journal of Trends in Computer Science and Smart Technology (TCSST), 2021, 3(01), 14-23.
  6. Grabisch M., Marichal J-L, Pap E. Aggregation functions, Cambridge University Press,
    2009, 127.
  7. Calvo T., Belyakov G. Aggregating functions based on penalties, Fuzzy sets and systems, 2010, 161:10, 1420-1436, DOI: 10.1016/j.fss.2009.05.012
  8. Mesiar R., et. al. A review of aggregation functions, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, 2008, 121-144.
  9. Yang F., Yang Zh., Cohen W. W. Differentiable learning of logical rules for reasoning in the knowledge base Advances in the field of neural information processing systems, 2017, 2320-2329.
  10. Flach P. Machine Learning: The Art and Science of Algorithms That Give Meaning to Data, Cambridge University Press, 2015. 400
  11. Akhlakur R., Sumaira T. Ensemble classifiers and their applications: a review, International Journal of Computer Trends and Technologies, 2014. 10, 31-35.
  12. Dyukova E. V., Zhuravlev Yu. I., Prokofiev P. A. Methods for improving the efficiency of logical correctors, Machine Learning and Data Analysis, 2015, 1:11, 1555-1583.
  13. Lyutikova L. A., Shmatova E. V. Algorithm for constructing logical operations to identify patterns in data, E3S Web of Conferences, 2020, 224, 01009 DOI: 10.1051/e3sconf/202022401009.
  14. Lyutikova L.A., Shmatova E.V. Analysis and synthesis of pattern recognition algorithms using variable logic, Information Technologies, 2016, 22:4, 292-297. (In Russian).
  15. Burges C. J. A tutorial on support vector machines for pattern recognition Data mining and knowledge discovery, 1998. 2:2, 121-167.
  16. Aladjev V. Computer Algebra System Maple: A New Software Library, ICCS 2003, Lecture Notes in Computer Science, Springer, Berlin, Heidelberg, 2003. 2657 DOI: 10.1007/3-540-44860-873
  17. Prepare S.A., Cook S.A., Rekhov R.A. Relative efficiency of systems of proof of statements, Journal of Symbolic Logic, 1979. 44:1.
  18. Duda R., Hart P. Pattern classification and scene analysis, Wiley New York, 1973. 3, 731-739.
  19. Lyutikova L. A. Using multivalued logic for qualitative data analysis, Journal of Physics: Conference Series. – IOP Publishing, 2021. 2131:3, 032046.
  20. Riazanov V. V., Sen’ko O. V., Zhuravlev Y. I. Recognition and prediction methods based on voting procedures, Pattern recognition and image analysis, 1999, 9:4, 713–718.
  21. Zhuravljov Ju. I. O konstruktivnom metode sinteza semejstv mazhoritarno pravil’nyh algoritmov [On a constructive method for synthesizing families of majority-correct algorithms]. Materialy konferencii VII Mezhdunarodnoj konferencii po raspoznavaniju obrazov i analizu izobrazhenij, 2004, vol. 1, pp. 113-115. (In Russian).

Lyutikova Larisa Adolfovna – Ph. D. (Phys.& Math.) Head of the Dep., Neural Networks and Machine Learning, Institute of Applied Mathematics and Automation, Kabardino-Balkar Republic, Nalchik, Russia, ORCID 0000-0003-4941-7854.