| Инд. авторы: | Barakhnin V.B., Kozhemyakina O.Yu., Rychkova E.V., Gladkikh A.S., Pastushkov I.S. |
| Заглавие: | Software for learning to solve problems of classification using of machine learning |
| Библ. ссылка: | Barakhnin V.B., Kozhemyakina O.Yu., Rychkova E.V., Gladkikh A.S., Pastushkov I.S. Software for learning to solve problems of classification using of machine learning // The European Proceedings of Social & Behavioural Sciences. - 2018. - Vol.XLIX. - P.106-112. - EISSN 2357-1330. - https://www.futureacademy.org.uk/files/images/upload/ICPE2018F012.pdf |
| Внешние системы: | DOI: 10.15405/epsbs.2018.11.02.12; РИНЦ: 36563737; |
| Реферат: | eng: At present time, the philologists analyze poetic texts in order to identify the various characteristics of the literature work (the information about the verse, the rhythmics of the end of poems, a detailed description of the used rhymes, etc.), which are necessary in the process of study of the author's work. One of the most important characteristics of a poetic text are its style and genre. Currently, the specialists in this field are forced to work with the classification almost manually. The information about the reasons of the provided predictions is not available to the user, although such knowledge is of great importance in further analysis of the results. These problems can be overcome by creating a software system that allows to load the categorized data, and to show a detailed report on the results of the classification on the output display. The purpose of this work is to implement a web application to control the algorithms for automatic determination of styles and genre types of poetic texts. It allows experts in the field of philology to download the necessary data in a convenient way, to choose an automatic classifier and to analyze valuably the result of the classification, based on its justification obtained by the LIME algorithm and its implementation in the ELI5 library. It is worth noting that this decision is not tied to any specific categories of classification, what makes it universal.
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| Издано: | 2018 |
| Физ. характеристика: | с.106-112 |
| Ссылка: | https://www.futureacademy.org.uk/files/images/upload/ICPE2018F012.pdf |
| Конференция: | Название: International Conference on Psychology and Education Аббревиатура: ICPE 2018 Город: Moscow Страна: Russia Даты проведения: 2018-06-25 - 2018-06-26 |
| Цитирование: | 1. Barakhnin, V., Kozhemyakina, O., & Pastushkov, I. (2017). Automated determination of the type of genre and stylistic coloring of Russian texts. ITM Web of Conferences, 10, 02001. doi: 10.1051/itmconf/20171002001. 2. Forcier, J., Bissex, P., & Chun, W. (2008). Python Web Development with Django. Addison-Wesley Professional. 3. Marrs, T. (2017). JSON at Work: Practical Data Integration for the Web. O'Reilly Media Inc 4. Obe, R., & Hsu, L. (2017). PostgreSQL: Up and Running: A Practical Guide to the Advanced Open Source Database. O'Reilly Media Inc. 5. Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). ACM. 6. Spurlock, J. (2013). Bootstrap: Responsive Web Development. O'Reilly Media Inc. |