A Survey on Evolutionary Multiobjective Feature Selection in Classification: Approaches, Applications, and Challenges

Maximizing the classification accuracy and minimizing the number of selected features are two primary objectives in feature selection (FS), which is inherently a multiobjective task. Multiobjective FS (MOFS) enables us to gain various insights from complex data in addition to dimensionality reduction and improved accuracy, which has attracted increasing attention from researchers and practitioners. Over the past two decades, significant advancements in MOFS in classification have been achieved in both the methodologies and applications, but have not been well summarized and discussed. To fill this gap, this article presents a broad survey on existing research on MOFS in classification, focusing on up-to-date approaches, applications, current challenges, and future directions. To be specific, we categorize MOFS in classification on the basis of different criteria, and provide detailed descriptions of representative methods in each category. Additionally, we summarize a list of successful real-world applications of MOFS from different domains, to exemplify their significant practical value and demonstrate their abilities in providing a set of tradeoff feature subsets to meet different requirements of decision makers. We also discuss key challenges and shed lights on emerging directions for future developments of MOFS.