RT info:eu-repo/semantics/article
T1 Feature selection : a perspective on inter-attribute cooperation
A1 Sosa Cabrera, Gustavo
A1 Guerrero, Santiago Gómez
A1 García Torres, Miguel
A1 Schaerer Serra, Christian Emilio
A2 Centro de Investigación en Matemática
AB High-dimensional datasets depict a challenge for learning tasks in data mining and machine learning. Feature selection is an effective technique in dealing with dimensionality reduction. It is often an essential data processing step prior to applying a learning algorithm. Over the decades, filter feature selection methods have evolved from simple univariate relevance ranking algorithms to more sophisticated relevance-redundancy trade-offs and to multivariate dependencies-based approaches in recent years. This tendency to capture multivariate dependence aims at obtaining unique information about the class from the intercooperation among features. This paper presents a comprehensive survey of the state-of-the-art work on filter feature selection methods assisted by feature intercooperation, and summarizes the contributions of different approaches found in the literature. Furthermore, current issues and challenges are introduced to identify promising future research and development.
PB Springer Nature
SN 2364-415X
YR 2023
FD 2023-08-15
LK http://hdl.handle.net/20.500.14066/4534
UL http://hdl.handle.net/20.500.14066/4534
LA eng
NO Sosa Cabrera, G., Gómez Guerrero, S., García Torres, M., & Schaerer, C. E. (2024). Feature selection: a perspective on inter-attribute cooperation. International Journal of Data Science and Analytics, 17, 139-151.https://doi.org/10.1007/s41060-023-00439-z
NO Corresponding author. Correspondence to Gustavo Sosa Cabrera, gdsosa@pol.una.py
NO Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 12-mar-2025