Preliminary Data Analysis and Feature Construction in Financial and Economic Information Processing Tasks
- Авторлар: Semenova P.A.1, Grineva N.V.1, Mikhaylova S.S.1
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Мекемелер:
- Financial University under the Government of the Russian Federation
- Шығарылым: Том 19, № 3 (2023)
- Беттер: 141-152
- Бөлім: Mathematical, Statistical and Instrumental Methods in Economics
- URL: https://journals.eco-vector.com/2541-8025/article/view/568338
- EDN: https://elibrary.ru/CALJPF
- ID: 568338
Дәйексөз келтіру
Ашық рұқсат
Рұқсат берілді
Аннотация
Machine learning is the main field of artificial intelligence. This contributes to a new stage in the development of the field of information technology, since now the computer is able to switch to self-learning mode without explicit programming. The aim of the study was to find the optimal set of exogenous variables that ensures the best quality of the model in the task of forecasting output volumes. As a result, several methods of constructing new attributes are implemented and the main aspects in the preprocessing of data from this subject area are highlighted.
Негізгі сөздер
Толық мәтін
Авторлар туралы
Polina Semenova
Financial University under the Government of the Russian Federation
Хат алмасуға жауапты Автор.
Email: 195229@edu.fa.ru
ORCID iD: 0009-0000-4835-5319
Faculty of Information Technology and Big Data Analysis
Ресей, MoscowNatalia Grineva
Financial University under the Government of the Russian Federation
Email: ngrineva@fa.ru
ORCID iD: 0000-0001-7647-5967
SPIN-код: 1140-9636
Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Data Analysis and Machine Learning
Ресей, MoscowSvetlana Mikhaylova
Financial University under the Government of the Russian Federation
Email: ssmihajlova@fa.ru
ORCID iD: 0000-0001-9183-8519
SPIN-код: 9697-3928
Dr. Sci. (Econ.), Professor, Professor of the Department of Data Analysis and Machine Learning
Ресей, MoscowӘдебиет тізімі
- In-depth guide to machine learning in the enterprise / Ed Burns —2021 —c. 1–3.
- Data Preprocessing and Data Wrangling in Machine Learning / Salvador García, Sergio Ramírez-Gallego, Julián Luengo, José Manuel Benítez, Francisco Herrera — November 2016.
- Big data preprocessing: methods and prospects / Jagreet Kaur —September 2022 —pp. 1–4.
- Bykov K. V. Features of data preprocessing for the application of machine learning / K. V. Bykov. —Text: direct // Young scientist. —2021. —№ 53 (395). —Pp. 1–4.
- Yu L. et al. Missing data preprocessing in credit classification: One-hot encoding or imputation? //Emerging Markets Finance and Trade. —2022. —V. 58. —№. 2. —pp. 472–482.
- Handling Categorical Data, The Right Way / Eugenio Zuccarelli — September 2020.
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