Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms Search (SOS) is one of the most successful meta-heuristic algorithms inspired by organisms' interaction in nature called mutualism, commensalism, and parasitism. In this paper, three SOS-based binary approaches are offered to solve the feature selection problem. In the first and second approaches, several S-shaped transfer functions and several Chaotic Tent Function-based V-shaped transfer functions called BSOSST and BSOSVT are used to make the binary SOS (BSOS). In the third approach, an advanced BSOS based on changing SOS and the chaotic Tent function operators called EBCSOS is provided. The EBCSOS algorithm uses the chaotic Tent function and the Gaussian mutation to increase usefulness and exploration. Moreover, two new operators, i.e., BMPT and BCPT, are suggested to make the commensalism and mutualism stage binary based on a chaotic function to solve the feature selection problem. Finally, the proposed BSOSST and BSOSVT methods and the advanced version of EBCSOS were implemented on 25 datasets than the basic algorithm's binary meta-heuristic algorithms. Various experiments demonstrated that the proposed EBCSOS algorithm outperformed other methods in terms of several features and accuracy. To further confirm the proposed EBCSOS algorithm, the problem of detecting spam E-mails was applied, with the results of this experiment indicating that the proposed EBCSOS algorithm significantly improved the accuracy and speed of all categories in detecting spam E-mails.

特征选择是在机器学习中预处理数据的主要步骤之一，其目标是通过删除其他嘈杂的特征来减少特征。数据集中的特征选择方法和特征约简必须考虑分类算法的准确性。元启发式算法是解决此问题的最成功，最有前途的方法。共生有机体搜索（SOS）是最成功的元启发式算法之一，其灵感来自有机体在自然界中的相互作用，称为共生，共鸣和寄生。本文提供了三种基于SOS的二进制方法来解决特征选择问题。在第一种和第二种方法中 几个S形传递函数和几个基于混沌帐篷函数的V形传递函数（称为BSOSST和BSOSVT）用于制作二进制SOS（BSOS）。在第三种方法中，提供了基于变化的SOS和称为EBCSOS的混乱Tent函数运算符的高级BSOS。EBCSOS算法使用混沌Tent函数和高斯突变来增加有用性和探索性。此外，提出了两个新的算子，即BMPT和BCPT，以基于混沌函数的共态和共生阶段为二元来解决特征选择问题。最后，与基本算法的二进制元启发式算法相比，在25个数据集上实现了所提出的BSOSST和BSOSVT方法以及EBCSOS的高级版本。各种实验表明，所提出的EBCSOS算法在一些功能和准确性方面都优于其他方法。为了进一步确认所提出的EBCSOS算法，应用了检测垃圾邮件的问题，实验结果表明，所提出的EBCSOS算法显着提高了所有类别的垃圾邮件检测的准确性和速度。