Discrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational ScienceDiscrete and Continuous Models and Applied Computational Science2658-46702658-7149Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)2421610.22363/2658-4670-2020-28-2-105-119Computer ScienceИнформатика и вычислительная техникаResearch ArticleComparative analysis of machine learning methods by the example of the problem of determining muon decayСравнительный анализ методов машинного обучения на примере задачи определения мюонного распадаGevorkyanMigran N.ГеворкянМ. Н.

Candidate of Sciences in Physics and Mathematics, Assistant Professor of Department of Applied Probability and Informatics

Кафедра прикладной информатики и теории вероятностей

gevorkyan-mn@rudn.ruDemidovaAnastasia V.ДемидоваА. В.

Candidate of Sciences in Physics and Mathematics, Assistant Professor of Department of Applied Probability and Informatics

Кафедра прикладной информатики и теории вероятностей

demidova-av@rudn.ruKulyabovDmitry S.КулябовД. С.

Docent, Doctor of Sciences in Physics and Mathematics, Professor at the Department of Applied Probability and Informatics

Кафедра прикладной информатики и теории вероятностей; Лаборатория информационных технологий

kulyabov-ds@rudn.ruPeoples’ Friendship University of Russia (RUDN University)Российский университет дружбы народовJoint Institute for Nuclear ResearchОбъединённый институт ядерных исследований15122020282VOL 28, NO2 (2020)ТОМ 28, №2 (2020)10511920072020Copyright ©; 2020, Gevorkyan M.N., Demidova A.V., Kulyabov D.S.Copyright ©; 2020, Геворкян М.Н., Демидова А.В., Кулябов Д.С.2020Gevorkyan M.N., Demidova A.V., Kulyabov D.S.Геворкян М.Н., Демидова А.В., Кулябов Д.С.http://creativecommons.org/licenses/by/4.0https://journals.rudn.ru/miph/article/view/24216

The history of using machine learning algorithms to analyze statistical models is quite long. The development of computer technology has given these algorithms a new breath. Nowadays deep learning is mainstream and most popular area in machine learning. However, the authors believe that many researchers are trying to use deep learning methods beyond their applicability. This happens because of the widespread availability of software systems that implement deep learning algorithms, and the apparent simplicity of research. All this motivate the authors to compare deep learning algorithms and classical machine learning algorithms. The Large Hadron Collider experiment is chosen for this task, because the authors are familiar with this scientific field, and also because the experiment data is open source. The article compares various machine learning algorithms in relation to the problem of recognizing the decay reaction  τ– →μ– + μ– + μ+ at the Large Hadron Collider. The authors use open source implementations of machine learning algorithms. We compare algorithms with each other based on calculated metrics. As a result of the research, we can conclude that all the considered machine learning methods are quite comparable with each other (taking into account the selected metrics), while different methods have different areas of applicability.

Применение алгоритмов машинного обучения для анализа статистических моделей имеет достаточно длинную историю. Развитие компьютерной техники дало этим алгоритмам новое дыхание. Особенно громкую известность получило такое направление машинного обучения, как глубинное обучение. Однако авторы полагают, что многие исследователи пытаются использовать методы глубинного обучения за пределами их применимости. Этому способствуют как широкая распространённость программных комплексов, реализующих алгоритмы глубинного обучения, так и кажущаяся простота исследования. Всё это стало побудительным мотивом для проведения сравнения алгоритмов глубинного обучения и классических алгоритмов машинного обучения. В качестве задачи был выбран эксперимент на Большом адронном коллайдере, поскольку авторы знакомы с данной научной областью, а также потому, что данные эксперимента доступны публично. В статье проводится сравнение различных алгоритмов машинного обучения применительно к задаче распознания реакции распада τ– →μ– + μ– + μ+ на Большом адронном коллайдере. Используются готовые свободные реализации алгоритмов машинного обучения. Алгоритмы сравниваются друг с другом на основе вычисляемых метрик. В результате исследования можно сделать вывод, что все рассмотренные методы машинного обучения вполне сопоставимы друг с другом (с учётом выбранных метрик), при этом разные методы имеют разные области применимости.

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