Feature selection methods in machine learning: A review
Reema Lalit, Nisha ., Nitin Nitin, А Р Хабибуллин · 2025
In practical applications, it has become increasingly important to accurately identify the relevant aspects of the data due to the rise in high dimensionality. There is no question about the significance of feature selection in this setting, and several techniques have been devised to effectively reduce data, and data pre-processing can effectively make use of feature selection (FS) approaches can be employed in. because there is such a large corpus of algorithms accessible, selecting the best FS approach is a difficult problem that must be tested in a variety of scenarios. This is helpful in locating precise data models. Numerous search strategies have been put forth in the literature since it is typically impractical to search broadly for the ideal feature subset. Tasks involving classification, grouping, and regression are where FS is most frequently used. This work includes the majority of commonly used feature selection techniques and pays particular attention to the components of the application. We cover advanced topics like standard filter, wrapper, and embedding methods along with FS for recent hybrid approaches.