Using multivalued logic for qualitative data analysis
Vestnik KRAUNC. Fiziko-matematičeskie nauki, Tome 40 (2022) no. 3, pp. 199-210 Cet article a éte moissonné depuis la source Math-Net.Ru

Voir la notice de l'article

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.
Keywords: intellectual system, predicate logic, predicate, decision function, class, subject area.
@article{VKAM_2022_40_3_a15,
     author = {L. A. Lyutikova},
     title = {Using multivalued logic for qualitative data analysis},
     journal = {Vestnik KRAUNC. Fiziko-matemati\v{c}eskie nauki},
     pages = {199--210},
     year = {2022},
     volume = {40},
     number = {3},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/VKAM_2022_40_3_a15/}
}
TY  - JOUR
AU  - L. A. Lyutikova
TI  - Using multivalued logic for qualitative data analysis
JO  - Vestnik KRAUNC. Fiziko-matematičeskie nauki
PY  - 2022
SP  - 199
EP  - 210
VL  - 40
IS  - 3
UR  - http://geodesic.mathdoc.fr/item/VKAM_2022_40_3_a15/
LA  - ru
ID  - VKAM_2022_40_3_a15
ER  - 
%0 Journal Article
%A L. A. Lyutikova
%T Using multivalued logic for qualitative data analysis
%J Vestnik KRAUNC. Fiziko-matematičeskie nauki
%D 2022
%P 199-210
%V 40
%N 3
%U http://geodesic.mathdoc.fr/item/VKAM_2022_40_3_a15/
%G ru
%F VKAM_2022_40_3_a15
L. A. Lyutikova. Using multivalued logic for qualitative data analysis. Vestnik KRAUNC. Fiziko-matematičeskie nauki, Tome 40 (2022) no. 3, pp. 199-210. http://geodesic.mathdoc.fr/item/VKAM_2022_40_3_a15/

[1] Zhuravlev Yu. I., “Ob algebraicheskom podkhode k resheniyu zadach raspoznavaniya ili klassifikatsii”, Problemy kibernetiki, 33 (1978), 5–68

[2] Shibzukhov Z. M., “Korrektnye algoritmy agregirovaniya operatsii”, Raspoznavanie obrazov i analiz izobrazhenii, 24:3 (2014), 377–382

[3] Naimi A. I., Balzer L. B., “Multilevel generalization: an introduction to super learning”, European Journal of Epidemiology, 33 (2018), 459–464 | DOI

[4] Haoxiang W., Smith S., “Big data analysis and perturbation using a data mining algorithm”, Journal of Soft Computing Paradigm (JSCP), 3 (01) (2021), 19-28 | DOI

[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), 3(01) (2021), 14-23 | DOI

[6] Grabisch M., Marichal J-L, Pap E., “Aggregation functions”, Cambridge University Press, 127 (2009)

[7] Calvo T., Belyakov G., “Aggregating functions based on penalties”, Fuzzy sets and systems, 161:10 (2010), 1420-1436 DOI: 10.1016/j.fss.2009.05.012 | DOI

[8] Mesiar R., Komornikova M., Kolesarova A., Calvo T., “A review of aggregation functions”, Fuzzy Sets and Their Extensions: Representation, Aggregation and Models, 2008, 121-144 | DOI

[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] Flakh P., Mashinnoe obuchenie: nauka i iskusstvo postroeniya algoritmov, kotorye izvlekayut znaniya iz dannykh, DMK Press, M., 2015, 400 pp.

[11] Akhlakur R., Sumaira T., “Ensemble classifiers and their applications: a review”, International Journal of Computer Trends and Technologies, 10 (2014), 31-35 | DOI

[12] Dyukova E. V., Zhuravlev Yu. I., Prokofev P. A., “Metody povysheniya effektivnosti logicheskikh korrektorov”, Mashinnoe obuchenie i analiz dannykh, 1:11 (2015), 1555-1583

[13] Lyutikova L. A., Shmatova E. V., “Algorithm for constructing logical operations to identify patterns in data”, E3S Web of Conferences, 224 (2020), 01009 DOI: 10.1051/e3sconf/202022401009 | DOI

[14] Lyutikova L. A., Shmatova E. V., “Analiz i sintez algoritmov raspoznavaniya obrazov s ispolzovaniem peremennoi logiki”, Informatsionnye tekhnologii, 22:4 (2016), 292-297

[15] Burges C. J., “A tutorial on support vector machines for pattern recognition”, Data mining and knowledge discovery, 2:2 (1998), 121-167 | DOI

[16] Aladjev V., “Computer Algebra System Maple: A New Software Library”, Lecture Notes in Computer Science, v. 2657 , Springer, Berlin, Heidelberg, 2003 DOI: 10.1007/3-540-44860-8_73

[17] Prepare S. A., Cook S. A., Rekhov R. A., “Relative efficiency of systems of proof of statements”, Journal of Symbolic Logic, 44:1 (1979)

[18] Duda R., Hart P., “Pattern classification and scene analysis”, Wiley New York, 3 (1973), 731-739

[19] Lyutikova L. A., “Using multivalued logic for qualitative data analysis”, Journal of Physics: Conference Series. IOP Publishing, 2131:3 (2021), 032046 | DOI

[20] Ryazanov V. V., Senko O. V., Zhuravlev Yu. V., “Metody raspoznavaniya i prognozirovaniya na osnove protsedur golosovaniya”, Raspoznavanie obrazov i analiz izobrazhenii, 9:4 (1999), 713-718

[21] Shibzukhov Z. M., “O konstruktivnom metode sinteza semeistv mazhoritarno pravilnykh algoritmov”, Materialy konferentsii VII Mezhdunarodnoi konferentsii po raspoznavaniyu obrazov i analizu izobrazhenii, v. 1, 2004, 113–115