TY - GEN

T1 - Approach to Research Feature Interactions

AU - Radeev, Nikita A.

N1 - Публикация для корректировки.

PY - 2023

Y1 - 2023

N2 - Feature crossing (or interaction) is one of the essential data analysis techniques provided to show insights into data by building interactions between features. It consists in creating combinations of original features from a dataset via different methods. Often, such combinations created with a strong domain knowledge contain important information for solving a problem. This process is quite creative and intuitive because number of all possible combinations can be huge and it can be impossible for a human to check all of them. This is highly dependent on the data used in the problem and domain, since even traits that are important statistically can be meaningless in the domain. In this work, we present an approach to the convenient exploration of feature crossings in data, their evaluation, ranking, and visualization. This approach implemented can be useful tool for data analysts. We present an example of the approach with a prototype implemented on Python 3 and Tableau Public on a real-world dataset.

AB - Feature crossing (or interaction) is one of the essential data analysis techniques provided to show insights into data by building interactions between features. It consists in creating combinations of original features from a dataset via different methods. Often, such combinations created with a strong domain knowledge contain important information for solving a problem. This process is quite creative and intuitive because number of all possible combinations can be huge and it can be impossible for a human to check all of them. This is highly dependent on the data used in the problem and domain, since even traits that are important statistically can be meaningless in the domain. In this work, we present an approach to the convenient exploration of feature crossings in data, their evaluation, ranking, and visualization. This approach implemented can be useful tool for data analysts. We present an example of the approach with a prototype implemented on Python 3 and Tableau Public on a real-world dataset.

UR - https://www.scopus.com/record/display.uri?eid=2-s2.0-85172017857&origin=inward&txGid=849696a6c64f44e10c1a7355c23fd5c0

UR - https://www.mendeley.com/catalogue/9d89d76e-74c3-35bc-b897-eab056dddd09/

U2 - 10.1109/edm58354.2023.10225150

DO - 10.1109/edm58354.2023.10225150

M3 - Conference contribution

SN - 9798350336870

SP - 1720

EP - 1724

BT - 24th IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2023; Novosibirsk; Russian Federation; 29 June 2023 до 3 July 2023

PB - Institute of Electrical and Electronics Engineers (IEEE)

ER -