Статистические методы оценки квартилей научных конференций

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В статье представлены результаты оценки квартилей научных конференций, выставленных ведущими рейтинговыми агентствами. Оценки получены на основе применения трёх методов многомерного статистического анализа: линейной регрессии, дискриминантного анализа и нейронных сетей. Для оценки использовалась обучающая выборка, включающая следующие факторы: возраст и периодичность конференции, количество участников и количество докладов, публикационная активность организаторов конференции, цитируемость докладов. В результате проведённого исследования линейная регрессионная модель подтвердила верность выставленных квартилей для 77% конференций, в то время как методы нейронных сетей и дискриминантного анализа дали близкие результаты, подтвердив верность выставленных квартилей для 81 и 85% конференций соответственно.

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1. Introduction As it is known [1], quartile (quarter) is a category of scientific publications, which is determined by bibliometric indicators reflecting, first of all, the level of citation, that is, the relevance of the publication by the scientific community. And if the procedure for assigning quartiles to scientific journals has long been developed and successfully applied in practice [2-5]. In addition, many metrics have been introduced to assess the impact of journals, such as impact factor, 5-year impact factor, immediacy index, and impact factor without self cites, median impact factor, aggregate impact factor and others [6]. At the same time, this issue remains the subject of research for scientific conferences [7-11]. Some rating agencies have already begun to rank scientific conferences without disclosing the details of this procedure. For example, there is a CORE conference ranking [12], a CCF conference ranking [13], and a Microsoft Academic conference ranking (has been deleted) [14]. The disadvantages of the first two ratings are that they are expert, regional and do not fully disclose the procedure for ranking conferences. They also rank only computer science conferences. Researchers use various methods to compile new conference rankings, such as correlation analysis [7, 15], statistical analysis [15, 16], calculation of indicators similar to journal ones [9], graph and tree analysis [8, 17], regression analysis [16], [11]. Many of these studies involved the use of several of the listed methods. There were also works devoted to the search for methods for predicting the rating of a conference or predicting the impact of works presented at a particular conference [18]. Machine learning was used for these purposes [19, 20]. Therefore, this study is devoted to comparing two popular methods for predicting conference rankings, and I also included in the study such a statistical method as discriminant analysis, which is essentially a mathematical prerequisite for machine learning. We managed to find data on some conferences via the Internet, including their quartiles and a number of other indicators, which will be discussed below. As a result, we received a training sample from 23 conferences, on the basis of which we will try to assess the adequacy of the quartiles exposed using three methods of multidimensional statistical analysis: linear regression, discriminant analysis and neural networks. © Ermolayeva A. M., 2024 This work is licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by-nc/4.0/legalcode 2. Training sample Let’s introduce the notation: -
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Об авторах

А. М. Ермолаева

Российский университет дружбы народов

Автор, ответственный за переписку.
Email: ermolaeva-am@rudn.ru
ORCID iD: 0000-0001-6107-6461

Assistant of Probability Theory and Cyber Security

ул. Миклухо-Маклая, д. 6, Москва, 117198, Российская Федерация

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Эта статья доступна по лицензии Creative Commons Attribution-NonCommercial 4.0 International License.