Preliminary Data Analysis and Feature Construction in Financial and Economic Information Processing Tasks
- Autores: Semenova P.A.1, Grineva N.V.1, Mikhaylova S.S.1
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Afiliações:
- Financial University under the Government of the Russian Federation
- Edição: Volume 19, Nº 3 (2023)
- Páginas: 141-152
- Seção: 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
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Resumo
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.
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Sobre autores
Polina Semenova
Financial University under the Government of the Russian Federation
Autor responsável pela correspondência
Email: 195229@edu.fa.ru
ORCID ID: 0009-0000-4835-5319
Faculty of Information Technology and Big Data Analysis
Rússia, MoscowNatalia Grineva
Financial University under the Government of the Russian Federation
Email: ngrineva@fa.ru
ORCID ID: 0000-0001-7647-5967
Código SPIN: 1140-9636
Cand. Sci. (Econ.), Associate Professor, Associate Professor of the Department of Data Analysis and Machine Learning
Rússia, MoscowSvetlana Mikhaylova
Financial University under the Government of the Russian Federation
Email: ssmihajlova@fa.ru
ORCID ID: 0000-0001-9183-8519
Código SPIN: 9697-3928
Dr. Sci. (Econ.), Professor, Professor of the Department of Data Analysis and Machine Learning
Rússia, MoscowBibliografia
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- 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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