Research into Machine Learning Algorithms for the Construction of Mathematical Models of Multimodal data Classification Problems

dc.citation.epage11
dc.citation.issue2
dc.citation.spage1
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorБойко, Наталія
dc.contributor.authorBoyko, Nataliya
dc.coverage.placenameЛьвів
dc.coverage.placenameLviv
dc.date.accessioned2023-04-26T08:15:41Z
dc.date.available2023-04-26T08:15:41Z
dc.date.created2021-10-10
dc.date.issued2021-10-10
dc.description.abstractСьогодні алгоритми машинного навчання (ML) все більше інтегруються у повсякденне життя. Можна навести безліч сфер сучасного життя, де вже застосовуються методи класифікації. Досліджуються методи, які враховують попередні передбачення та помилки, які обчислюються в результаті інтегрування даних задля отримання прогнозів, для отримання результату класифікації. Проведено загальний огляд методів класифікації. Здійснено експерименти над алгоритмами машинного навчання для мультимодальних даних. Важливо враховувати всі характеристики метрик та ознак під час використання алгоритмів ML для прогнозування мультимодальних даних. В роботі проаналізовано основні переваги та недоліки алгоритмів Gradient Boosting, Random Forest, Logistic Regression та XGBoost.
dc.description.abstractCurrently, machine learning algorithms (ML) are increasingly integrated into everyday life. There are many areas of modern life where classification methods are already used. Methods taking into account previous predictions and errors that are calculated as a result of data integration to obtain forecasts for obtaining the classification result are investigated. A general overview of classification methods is conducted. Experiments on machine learning algorithms for multimodal data are performed. It is important to consider all the characteristics of metrics and features when using ML algorithms to predict multimodal data. The main advantages and disadvantages of Gradient Boosting, Random Forest, Logistic Regression and XGBoost algorithms are analyzed in the work.
dc.format.extent1-11
dc.format.pages11
dc.identifier.citationBoyko N. Research into Machine Learning Algorithms for the Construction of Mathematical Models of Multimodal data Classification Problems / Nataliya Boyko // Computational Problems of Electrical Engineering. — Lviv : Lviv Politechnic Publishing House, 2021. — Vol 11. — No 2. — P. 1–11.
dc.identifier.citationenBoyko N. (2021) Research into Machine Learning Algorithms for the Construction of Mathematical Models of Multimodal data Classification Problems. Computational Problems of Electrical Engineering (Lviv), vol. 11, no 2, pp. 1-11.
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/58462
dc.language.isoen
dc.publisherВидавництво Львівської політехніки
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofComputational Problems of Electrical Engineering, 2 (11), 2021
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dc.relation.referencesen[1] "Dataset South African Heart Disease", https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/76SIQD
dc.relation.referencesen[2] "Open Machine Learning Course: Gradient Boosting Machines", http://uc-r.github.io/gbm_regression
dc.relation.referencesen[3] P. Rathi and A. Sharma, "A review paper on prediction of diabetic retinopathy using data mining techniques", in International journal of innovative research in technology, Vol. 4, pp. 292–297, 2017.
dc.relation.referencesen[4] N. Boyko and K. Boksho, "Application of the Naive Bayesian Classifier in Work on Sentimental Analysis of Medical Data", in Proc. 3rd International Conference on Informatics & Data-Driven Medicine (IDDM 2020), Växjö, Sweden, pp. 230–239, 2020.
dc.relation.referencesen[5] C. Maklin, "XGBoost Python Example", https:// towardsdatascience.com/xgboost-python-example42777d01001e, last accessed 2020/12/21.
dc.relation.referencesen[6] R. M. V. Humphris, Testing Algorithm Fairness Metrics for Binary Classification Problems by Supervised Machine Learning Algorithms, Vrije Universiteit Amsterdam, 2020.
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dc.relation.referencesen[10] J. Brownlee, "A gentle introduction to the bootstrap method", https://machinelearningmastery.com/ agentle-introduction-to-the-bootstrap-method/, last accessed 2020/06/29.
dc.relation.referencesen[11] A. Chakure, "Decision tree classification", https://towardsdatascience. com/decision-treeclassification-de64fc4d5aac, last accessed 2019/11/28.
dc.relation.referencesen[12] C. Cortes and V. N. Vapnik, "Support-vector networks", Machine Learning, Vol. 20(3), pp. 273–297, 1995. doi: https://doi.org/10.1023/ A:1022627411411.
dc.relation.referencesen[13] N. Boyko, "Information system of catering selection by using clustering analysis", in 2018 IEEE Ukraine Student, Young Professional and Women in Engineering Congress (UKRSYW) October 26, Kyiv, Ukraine, pp. 7–13, 2018.
dc.relation.referencesen[14] "DataCamp. Hyperparameter tuning with randomizedsearchcv", https: //campus.datacamp. com/ courses/supervised-learning-with-scikit-learn/ fine-tuning-your-model?ex=11, last accessed 2020/06/11.
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dc.relation.urihttps://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/76SIQD
dc.relation.urihttp://uc-r.github.io/gbm_regression
dc.relation.urihttps://medium.com/
dc.relation.urihttps://machine
dc.relation.urihttps://machinelearningmastery.com/
dc.relation.urihttps://towardsdatascience
dc.relation.urihttps://doi.org/10.1023/
dc.relation.urihttps://www.dezyre.com/data-science-inpython-tutorial/
dc.rights.holder© Національний університет „Львівська політехніка“, 2021
dc.subjectclassification
dc.subjectbinary classification
dc.subjectgradient boosting
dc.subjectrandom forest
dc.subjectlogistic regression
dc.subjectX
dc.titleResearch into Machine Learning Algorithms for the Construction of Mathematical Models of Multimodal data Classification Problems
dc.title.alternativeДослідження алгоритмів машинного навчання для побудови математичних моделей задач класифікації мультимодальних даних
dc.typeArticle

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