Informatics and Applications
2024, Volume 18, Issue 3, pp 61-68
CORRECT SUPERVISED CLASSIFICATION: JSM-METHOD OVER PRODUCT OF PARTIAL ORDERS
- E. V. Djukova
- G. O.Masliakov
- D. S. Ianakov
Abstract
The authors consider a logical approach to the supervised classification problem. A difference and
a connection between two well-known directions of logical classification are noted: the direction represented by
Correct Voting Procedures (CVP) and the direction based on the ideas of the JSM-method. The issues of improving
the classifiers of the second direction are considered on the basis of using a less strict decisive rule and generalizing
a scheme of work in the case when the featured descriptions of the objects under study are the elements of the
Cartesian product of finite partially ordered sets. The developed new models of the JSM-classifiers use the ideas
proposed earlier when creating similar algorithms for the CVP direction. The results of an experimental study on
real-world tasks using a special linear ordering of feature values are presented.
[+] References (16)
- Vapnik, V.N., and A. Ya. Chervonenkis. 1974. Teoriya
raspoznavaniya obrazov. Statisticheskie problemy obucheniya
[Pattern recognition theory (statistical learning problems)].
Moscow: Nauka. 416 p.
- Bishop,C.M. 2006. Pattern recognition andmachine learning.
Information science and statistics ser. New York, NY:
Springer-Verlag. 778 p.
- Theodoridis, S. 2009. Pattern recognition. 4th ed. Burlington,
MA: Academic Press. 961 p.
- Bosov, A. V., and A. V. Ivanov. 2022. Tekhnologiya klassifikatsii
tipov kontenta elektronnogo uchebnika [Technology
for classification of content types of e-textbooks].
Informatika i ee Primeneniya—Inform. Appl. 16(4):63–72.
doi: 10.14357/19922264220410. EDN: YERCNH.
- Arkhipov, P.O., S. L. Filippskikh, and M. V. Tsukanov.
2023. Razrabotka novoy modeli stupenchatoy svertochnoy
neyronnoy seti dlya klassifikatsii anomaliy na panoramakh
[Development of a new model of step convolutional neural
network for classification of anomalies on panoramas].
Informatika i ee Primeneniya—Inform. Appl. 17(1):50–56.
doi: 10.14357/19922264230107. EDN: FHNUPW.
- Tuboltsev, V. P., A. V. Lapko, and V. A. Lapko. 2023.
Neparametricheskiy algoritm avtomaticheskoy klassifikatsii
dannykh distantsionnogo zondirovaniya [Nonparametric
algorithm for automatic classification of
remote sensing data]. Informatika i ee Primeneniya — Inform.
Appl. 17(4):23–31. doi: 10.14357/19922264230404.
EDN:MPEWAW.
- Finn, V.K. 1976. O vozmozhnosti formalizatsii pravdopodobnykh
rassuzhdeniy sredstvami mnogoznachnykh
logic [On the possibility of formalizing plausible reasoning
by means of multivalued logics]. Vsesoyuzn. simpozium
po logike i metodologii nauki [All-Union Symposium on
the Logic and Methodology of Science]. Kiev: Naukova
dumka. 82–83.
- Djukova, E., G.Masliakov, and A. Djukova. 2023. On the
logical classification of integer data. 9th Conference (International)
on Information Technology and Nanotechnology
Proceedings. Piscataway, NJ: IEEE. Art. 10139273. 4 p.
doi: 10.1109/ITNT57377.2023.10139273.
- Djukova, E.V., G. O. Maslyakov, and A. P.Djukova. 2023.
Logicheskie metody korrektnoy klassifikatsii dannykh
[Logical methods of correct data classification]. Informatika
i ee Primeneniya—Inform. Appl. 17(3):64–70. doi:
10.14357/19922264230309. EDN: OZHXOX.
- Baskalova, L. V., and Yu. I. Zhuravlev. 1981. A model of
recognition algorithms with representative samples and
systems of supporting sets. USSR Comp. Math. Math.
21(5):189–199. doi: 10.1016/0041-5553(81)90109-9.
- Dyukova, E. V., and N. V. Peskov. 2002. Search for informative
fragments in descriptions of objects in discrete recognition procedures. Comp. Math. Math. Phys.
42(5):711–723. EDN: LHJOVR.
- Djukova, E. V., G. O. Masliakov, and P. A. Prokofyev. 2019.
On the logical analysis of partially ordered data in the supervised
classification problem. Comp. Math. Math. Phys.
59(9):1542–1552. doi: 10.1134/S0965542519090082.
EDN: KMZRFX.
- Djukova, E. V., and Y. I. Zhuravlev. 2018. Monotone dualization
problem and its generalizations: Asymptotic estimates
of the number of solutions. Comp.Math.Math. Phys.
58(12):2064–2077. doi: 10.1134/S0965542518120102. EDN: YUPYKW.
- Gnatyshak, D. V., D. V. Ignatov, S. O. Kuznetsov, and
B.G. Mirkin. 2015. Triadic formal concept analysis and
triclustering: Searching for optimal patterns. Mach.Learn.
101:271–302. doi: 10.1007/s10994-015-5487-y.
- Djukova, E. V., and G. O. Maslyakov. 2021. O vybore
chastichnykh poryadkov na mnozhestvakh znacheniy priznakov
v zadache klassifikatsii [On the choice of partial
orders on feature values sets in the supervised classification
problem]. Informatika i ee Primeneniya — Inform.
Appl. 15(4):72–78. doi: 10.14357/19922264210410.EDN: UVJFGF.
- Kuznetsov, S.O. 1993. Fast algorithm for construction
of all intersections of objects from a finite semilattice.
Automatic Documentation Mathematical Linguistics 27(5):23–28.
[+] About this article
Title
CORRECT SUPERVISED CLASSIFICATION: JSM-METHOD OVER PRODUCT OF PARTIAL ORDERS
Journal
Informatics and Applications
2024, Volume 18, Issue 3, pp 61-68
Cover Date
2024-09-20
DOI
10.14357/19922264240308
Print ISSN
1992-2264
Publisher
Institute of Informatics Problems, Russian Academy of Sciences
Additional Links
Key words
supervised classification; logical classifier; Correct Voting Procedures; JSM-method; representative elementary classifier; partial order
Authors
E. V. Djukova  , G. O.Masliakov , and D. S. Ianakov 
Author Affiliations
Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences, 44-2 Vavilov Str., Moscow 119333, Russian Federation
National Research University Higher School of Economics, 20 Myasnitskaya Str., Moscow 101000, Russian Federation
|