Войти
Algorithms for big data mining of hub patent transactions based on decision trees  id статьи: 3403
Тип публикации
материалы конференции
Язык
En
Журнал
EPJ Web of Conferences. 3rd International Conference on Advances in Applied Physics and Mathematics for Energy, Environment and Earth Science, AAPM-III 2025. Tashkent 20 January 2025 до 21 January 2025

ISSN:21016275
Год
2025
Выходные данные
том 318
выпуск
страницы № 04013
Авторы
Zhukov, A.1
Pronichkin, S.3
Mihaylov, Y.1
EDN
Абстракт
Dysfunctions of the patent supply and demand market have a negative impact on the sustainability of the national innovation system, which stimulates the growth of prices for knowledge-intensive products. It is necessary to establish a relationship between fiscal decisions regarding patent transactions and the prospects for the development of commercialization of the results of intellectual activity. One of the most promising methods for improving the accuracy of system analysis of big and semi-structured patent transaction data is the use of decision trees. Existing methods based on the error backpropagation method are quite slow, and their accelerated versions lose in training accuracy. To effectively solve the problem of forecasting the cost of hub patent transactions, parametric algorithms have been developed based on response bias and with the additional use of predicative structures of the model of successive geometric transformations. The optimal number of decision tree predicates has been established taking into account computational efforts and the accuracy of forecasting the cost of hub patent transactions. Based on evolutionary computing, the optimal values of the parameters of algorithms for big data mining of hub patent transactions have been established.
Ключевые слова
Дата занесения
2025-04-11 11:55:46 (квартал- I)
Scopus
Статус есть
Квартиль --
WoS
Статус нет
Квартиль --
РИНЦ
Статус нет
Импакт-фактор --
количество баллов за публикацию
0
количество баллов каждому автору
0
Финансирование:

124013000662-7