Stable ant‐antlion optimiser for feature selection on high‐dimensional data

Abstract High‐dimensional data exists widely in the real world, such as gene, magnetic resonance imaging (MRI), text, web data and so on. Feature selection is an effective and powerful method that is often adopted to reduce dimensions of high‐dimensional data for promoting learning algorithm's ability to obtain useful information from them. However, feature selection stability lacks attention for a long time, which plays an important role in getting compelling results. This study proposes a stable feature selection approach called stable ant‐antlion optimiser (SALO) that is a modified hybrid evolutionary method combining ant colony optimisation and antlion optimiser. Several relative stable filter methods are employed to rank features as guide information, fisher discriminant rate is used to evaluate features as heuristic information, and F1 indicator and the similarity between feature subset and stable ranked feature list are taken as optimisation objects. Comprehensive experiments show the fantasy stability and excellent classification performance of our proposed sophisticated method.