In the past decades, the rapid growth of computer and database technologies has led to the rapid growth of large-scale datasets. On the other hand, data mining applications with high dimensional datasets that require high speed and accuracy are rapidly increasing. An important issue with these applications is the curse of dimensionality, where the number of features is much higher than the number of patterns. One of the dimensionality reduction approaches is feature selection that can increase the accuracy of the data mining task and reduce its computational complexity. The feature selection method aims at selecting a subset of features with the lowest inner similarity and highest relevancy to the target class. It reduces the dimensionality of the data by eliminating irrelevant, redundant, or noisy data. In this paper, a comparative analysis of different feature selection methods is presented, and a general categorization of these methods is performed. Moreover, in this paper, state-of-the-art swarm intelligence is studied, and the recent feature selection methods based on these algorithms are reviewed. Furthermore, the strengths and weaknesses of the different studied swarm intelligence-based feature selection methods are evaluated.

在过去的几十年中，计算机和数据库技术的迅速发展导致大规模数据集的迅速发展。另一方面，具有高速和高精度的高维数据集的数据挖掘应用程序正在迅速增加。这些应用程序的一个重要问题是维数的诅咒，其中特征的数量远多于图案的数量。降维方法之一是特征选择，它可以提高数据挖掘任务的准确性并降低其计算复杂性。特征选择方法旨在选择与目标类别具有最低内部相似度和最高相关性的特征子集。通过消除无关，冗余或嘈杂的数据，它降低了数据的维数。在本文中，给出了不同特征选择方法的比较分析，并对这些方法进行了一般分类。此外，本文研究了最新的群智能，并综述了基于这些算法的最新特征选择方法。此外，评估了不同研究的基于群体智能的特征选择方法的优缺点。