One of the major problems in Big Data is a large number of features or dimensions, which causes the issue of “the curse of dimensionality” when applying machine learning, especially classification algorithms. Feature selection is an important technique which selects small and informative feature subsets to improve the learning performance. Feature selection is not an easy task due to its large and complex search space. Recently, swarm intelligence techniques have gained much attention from the feature selection community because of their simplicity and potential global search ability. However, there has been no comprehensive surveys on swarm intelligence for feature selection in classification which is the most widely investigated area in feature selection. Only a few short surveys is this area are still lack of in-depth discussions on the state-of-the-art methods, and the strengths and limitations of existing methods, particularly in terms of the representation and search mechanisms, which are two key components in adapting swarm intelligence to address feature selection problems. This paper presents a comprehensive survey on the state-of-the-art works applying swarm intelligence to achieve feature selection in classification, with a focus on the representation and search mechanisms. The expectation is to present an overview of different kinds of state-of-the-art approaches together with their advantages and disadvantages, encourage researchers to investigate more advanced methods, provide practitioners guidances for choosing the appropriate methods to be used in real-world scenarios, and discuss potential limitations and issues for future research.

大数据中的主要问题之一是大量的特征或维度，这导致在应用机器学习（尤其是分类算法）时出现“维度诅咒”的问题。特征选择是一项重要的技术，它选择小的信息量丰富的特征子集以提高学习性能。特征选择由于其庞大而复杂的搜索空间而并非易事。最近，群体智能技术因其简单性和潜在的全局搜索能力而备受特征选择社区的关注。但是，还没有关于群体智能的综合调查来进行分类中的特征选择，这是特征选择研究最广泛的领域。该领域仅进行了几项简短的调查，仍然缺乏对最新方法以及现有方法的优势和局限性的深入讨论，特别是在表示和搜索机制方面，这是两个关键群体智能中的组件，以解决特征选择问题。本文对利用群体智能实现分类中特征选择的最新技术进行了全面的调查，重点是表示和搜索机制。期望概述各种不同的最新方法以及它们的优点和缺点，鼓励研究人员研究更高级的方法，为从业人员提供指导，以选择在实际场景中使用的适当方法，