Geodesic
Research of the performance of machine learning algorithms in data classification problems
Problemy fiziki, matematiki i tehniki, no. 4 (2023), pp. 94-102.

Voir la notice de l'article provenant de la source Math-Net.Ru

An approach to solving the problem of constructing machine learning models in solving data classification problems is proposed. Using the example of analyzing biomedical data sets, the performance of machine learning algorithms tuned using pre-optimized hyperparameters is compared. The best values of hyperparameters that provide effective prediction were found for the most common machine learning algorithms.
Keywords: machine learning, data classification, hyperparameter optimization, big data processing, disease prediction. 
@article{PFMT_2023_4_a15,
     author = {E. V. Timoschenko and A. F. Razhkov},
     title = {Research of the performance of machine learning algorithms in data classification problems},
     journal = {Problemy fiziki, matematiki i tehniki},
     pages = {94--102},
     publisher = {mathdoc},
     number = {4},
     year = {2023},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/PFMT_2023_4_a15/}
}
TY  - JOUR
AU  - E. V. Timoschenko
AU  - A. F. Razhkov
TI  - Research of the performance of machine learning algorithms in data classification problems
JO  - Problemy fiziki, matematiki i tehniki
PY  - 2023
SP  - 94
EP  - 102
IS  - 4
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/PFMT_2023_4_a15/
LA  - ru
ID  - PFMT_2023_4_a15
ER  - 
%0 Journal Article
%A E. V. Timoschenko
%A A. F. Razhkov
%T Research of the performance of machine learning algorithms in data classification problems
%J Problemy fiziki, matematiki i tehniki
%D 2023
%P 94-102
%N 4
%I mathdoc
%U http://geodesic.mathdoc.fr/item/PFMT_2023_4_a15/
%G ru
%F PFMT_2023_4_a15
E. V. Timoschenko; A. F. Razhkov. Research of the performance of machine learning algorithms in data classification problems. Problemy fiziki, matematiki i tehniki, no. 4 (2023), pp. 94-102. http://geodesic.mathdoc.fr/item/PFMT_2023_4_a15/

[1] A. Rajkomar, J. Dean, I. Kohane, “Machine Learning in Medicine”, The New Englang journal of medicine, 380:14, Apr 4. (2019), 1347–1358 | DOI

[2] H. Almarabeh, E. Amer, “A study of data mining techniques accuracy for healthcare”, International Journal of Computer Applications, 168:3, Jun (2017), 12–17 | DOI

[3] E.V. Timoschenko, A.F. Razhkov, “Metody intellektualnogo analiza dannykh v virtualnom praktikume dlya tselei tsifrovizatsii obrazovaniya”, Tsifrovaya transformatsiya, 2021, no. 4, 52–62

[4] E.V. Timoschenko, A.F. Razhkov, Intellektualnyi analiz dannykh: laboratornyi praktikum, MGU imeni A.A. Kuleshova, Mogilev, 2022, 72 pp.

[5] A.F. Razhkov, E.V. Timoschenko, “Optimizatsiya giperparametrov algoritmov mashinnogo obucheniya dlya resheniya zadach klassifikatsii dannykh”, Sovremennoe programmirovanie, materialy IV Mezhdunarodnoi nauchno-prakticheskoi konferentsii (g. Nizhnevartovsk, 8 dekabrya 2021 goda), ed. T.B. Kaziakhmedov, Nizhnevartovskii gosudarstvennyi universitet, Nizhnevartovsk, 2022, 267–274

[6] Skobtsov V. Yu., “Neirosetevye modeli dlya binarnoi klassifikatsii dannykh telemetricheskoi informatsii malykh kosmicheskikh apparatov”, Informatsionnye tekhnologii i sistemy 2021 (ITS 2021):, materialy mezhdunarodnoi nauchnoi konferentsii (Minsk, 24 noyabrya 2021 g.), eds. L.Yu. Shilin i dr., Belorusskii gosudarstvennyi universitet informatiki i radioelektroniki, Minsk, 2021, 98–100

[7] Ali Usman, Shamsi Mohammad Haris, Bohacek Mark, Purcell Karl, Hoare Cathal, Mangina Eleni, O Donnell James, “A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making”, Applied Energy, 2020 | DOI

[8] P.C. Sen, M. Hajra, M. Ghosh, “Supervised Classification Algorithms in Machine Learning: A Survey and Review”, Emerging Technology in Modelling and Graphics, Advances in Intelligent Systems and Computing, 937, Springer Nature, Singapore, 2020, 99–111 | DOI

[9] UCI Machine Learning Repository, (Date of access: 20.09.2023) https://archive.ics.uci.edu/

[10] E.V. Timoschenko, A.F. Razhkov, “Metody intellektualnogo analiza biomeditsinskikh dannykh”, Itogi nauchnykh issledovanii uchenykh MGU im. A.A. Kuleshova 2019 g., materialy nauchno-metodicheskoi konferentsii (g. Mogilev, 29 yanvarya–10 fevralya 2020 goda), Mogilev, 2020, 106–107

[11] K. Balasree, K. Dharmarajan, “Big Data on Machine Learning - A Review”, Engineering and Scientific International Journal, 8:3 (2021), 86–91

[12] S.N. Bardab, T.M. Ahmed, T.A.A. Mohammed, “Data mining classification algorithms: An overview”, International Journal of Advanced and Applied Sciences, 8:2 (2021), 1–5

[13] M. Fatima, M. Pasha, “Survey of machine learning algorithms for disease diagnostic”, Journal of Intelligent Learning Systems and Applications, 9:1 (2017), 1–16 | DOI | MR

[14] Johann Faouzi, Olivier Colliot, “Classic machine learning algorithms”, Machine Learning for Brain Disorders. Neuromethods, 197 (2023) | DOI | Zbl

[15] S. Raschka, J. Patterson, C. Nolet, “Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence”, Information, 11:4 (2020), 193 | DOI

[16] B.J. Erickson, F. Kitamura, “Magician's Corner: 9”, Performance Metrics for Machine Learning Models. Radiology. Artificial Intelligence, 3:3 (2021) | DOI | Zbl