Preliminary Data Analysis and Feature Construction in Financial and Economic Information Processing Tasks
( Pp. 141-152)
More about authors
Polina A. Semenova
the Faculty of Information Technology and Big Data Analysis
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Grineva Natalia V. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Data Analysis and Machine Learning; Financial University under the Government of the Russian Federation; Moscow, Russian Federation Mikhaiylova Svetlana S. Dr. Sci. (Econ.), Associate Professor; Professor, Department of Data Analysis and Machine Learning, Faculty of Information Technology and Big Data Analysis
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Grineva Natalia V. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Data Analysis and Machine Learning; Financial University under the Government of the Russian Federation; Moscow, Russian Federation Mikhaiylova Svetlana S. Dr. Sci. (Econ.), Associate Professor; Professor, Department of Data Analysis and Machine Learning, Faculty of Information Technology and Big Data Analysis
Financial University under the Government of the Russian Federation
Moscow, Russian Federation
Abstract:
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.
How to Cite:
Semenova P. A., Grineva N. V., Mikhaylova S. S. Preliminary Data Analysis and Feature Construction in Financial and Economic Information Processing Tasks // ECONOMIC PROBLEMS AND LEGAL PRACTICE. 2023. Vol. 19. № 3. P. 141-152. (in Russ.) EDN: CALJPF
Reference list:
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.
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.
Keywords:
data preprocessing, feature construction, supply volumes..
Related Articles
