Stable feature selection based on brain storm optimisation for high-dimensional data

Feature selection is a widely used data pre-processing method. However, the research on feature selection stability is very rare. Although there are some related studies that mainly focus on filters rather than wrappers, especially wrappers based on evolutionary algorithms. In this paper, a stable feature selection method called stable brain storm optimisation (SBSO) is proposed. It puts forward a new population initialisation strategy, which treats stable feature selection results as guide information for initialisation and utilises such chaos to initialise population. SBSO also sets up an information archive to store the historical optimal individuals dynamically. A large number of experiments show that SBSO gives excellent classification performance when compared with other methods, and superior feature selection stability when compared with other wrappers.