One method to increase classifier accuracy is using Feature Selection (FS). The main idea in the FS is reducing complexity, eliminating irrelevant information, and deleting a subset of input features that either have little information or have no information for prediction. In this paper, three efficient binary methods based on the Symbiotic Organisms Search (SOS) algorithm were presented for solving the FS problem. In the first and second methods, several S_shaped and V_shaped transfer functions were used for the binarization of the SOS, respectively. These methods were called BSOSS and BSOSV. In the third method, two new operators called Binary Mutualism Phase (BMP) and Binary Commensalism Phase (BCP) were presented for binarization of the SOS, named Efficient Binary SOS (EBSOS). The proposed methods were run on 18 standard UCI datasets and compared to the base and important meta-heuristic algorithms. The test results showed that the EBSOS method has the best performance among the three proposed methods for the binarization of the SOS. Finally, the EBSOS method was compared to the Genetic Algorithm (GA), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO) Algorithm, Binary Flower Pollination Algorithm (BFPA), Binary Grey Wolf Optimizer (BGWO) Algorithm, Binary Dragonfly Algorithm (BDA), and Binary Chaotic Crow Search Algorithm (BCCSA). In addition, the EBSOS method was executed on the spam email dataset with the KNN, NB, SVM, and MLP classifiers. The results showed that the EBSOS method has better performance compared to other methods in terms of feature count and accuracy criteria. Furthermore, it was practically evaluated on spam email detection in particular.

中文翻译：

---

用于垃圾邮件检测的二元共生生物搜索算法的特征选择

提高分类器准确性的一种方法是使用特征选择 (FS)。FS 的主要思想是降低复杂度，消除不相关的信息，并删除具有少量信息或没有用于预测的信息的输入特征子集。在本文中，提出了三种基于共生生物搜索 (SOS) 算法的有效二进制方法来解决 FS 问题。在第一种和第二种方法中，分别使用了几个 S 形和 V 形传递函数来对 SOS 进行二值化。这些方法被称为 BSOSS 和 BSOSV。在第三种方法中，提出了两个新的算子，称为二元互惠阶段 (BMP) 和二元共生阶段 (BCP)，用于 SOS 的二值化，称为高效二元 SOS (EBSOS)。所提出的方法在 18 个标准 UCI 数据集上运行，并与基本和重要的元启发式算法进行了比较。测试结果表明，在提出的三种 SOS 二值化方法中， EBSOS 方法的性能最好。最后，将 EBSOS 方法与遗传算法 (GA)、二元蝙蝠算法 (BBA)、二元粒子群优化 (BPSO) 算法、二元花授粉算法 (BFPA)、二元灰狼优化器 (BGWO) 算法、二元蜻蜓算法进行了比较算法 (BDA) 和二元混沌乌鸦搜索算法 (BCCSA)。此外，使用 KNN、NB、SVM 和 MLP 分类器在垃圾邮件数据集上执行 EBSOS 方法。结果表明，与其他方法相比，EBOSS方法在特征计数和准确度标准方面具有更好的性能。此外，