A hybrid genetic algorithm with wrapper-embedded approaches for feature selection - Liang Yong

Feature selection is an important research area for big data analysis. In recent years, various feature selection approaches have been developed, which can be divided into four categories: filter, wrapper, embedded, and combined methods. In the combined category, many hybrid genetic approaches from evolutionary computations combine filter and wrapper measures of feature evaluation to implement a population-based global optimization with efficient local search. However, there are limitations to existing combined methods, such as the two-stage and inconsistent feature evaluation measures, difficulties in analyzing data with high feature interaction, and challenges in handling large-scale features and instances. Focusing on these three limitations, we proposed a hybrid genetic algorithm with wrapper-embedded feature approach for selection approach (HGAWE), which combines genetic algorithm (global …