A Brief Survey on Nature-Inspired Metaheuristics for Feature Selection in Classification in this Decade

Feature selection (FS) has been a hot topic in the past few decades. More and more researches spring up due to the fast development of data and computer science. Feature selection as a preprocessing technique plays an important role in improving the efficiency of data mining and analysis over substantial real-world applications. This paper provides a brief survey on feature selection based on nature-inspired metaheuristics for classification in this decade to detail the newest researches. The main challenges facing researchers are concluded as accuracy, stability, scalability and computational cost. Related work trying to solve these challenges is discussed.