A survey on swarm intelligence approaches to feature selection in data mining
A survey on swarm intelligence approaches to feature selection in data mining
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DOI：
10.1016/j.swevo.2020.100663
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发表时间：
2020-05-01
影响因子：
10
通讯作者：
Zhang, Mengjie
中科院分区：
文献类型：
--
作者：
Bach Hoai Nguyen;Xue, Bing;Zhang, Mengjie
大数据中的一大问题是大量的特征或维度，这导致在应用机器学习时，特别是分类算法时，会出现“维度诅咒”的问题。特征选择是选择信息量小的特征子集来提高学习性能的一种重要技术。特征选择由于其庞大而复杂的搜索空间，并不是一件容易的事情。近年来，群体智能技术因其简单性和潜在的全局搜索能力而受到特征选择领域的广泛关注。然而，作为特征选择领域中研究最广泛的领域，群智能在分类特征选择中的应用还没有得到全面的研究。只有几个简短的调查，这一领域仍然缺乏对最新方法的深入讨论，以及现有方法的优势和局限性，特别是在表示和搜索机制方面，这是采用群体智能来解决特征选择问题的两个关键组成部分。本文对利用群体智能实现分类特征选择的研究现状进行了综述，重点研究了群体智能在分类中的表示和搜索机制。期望对各种最先进的方法及其优缺点进行概述，鼓励研究人员研究更先进的方法，为实践者提供指导，选择在现实世界中使用的适当方法，并讨论未来研究的潜在限制和问题。
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.