Feature selection (FS) was regarded as a global combinatorial optimization problem. FS is used to simplify and enhance the quality of high-dimensional datasets by selecting prominent features and removing irrelevant and redundant data to provide good classification results. FS aims to reduce the dimensionality and improve the classification accuracy that is generally utilized with great importance in different fields such as pattern classification, data analysis, and data mining applications. The main problem is to find the best subset that contains the representative information of all the data. In order to overcome this problem, two binary variants of the whale optimization algorithm (WOA) are proposed, called bWOA-S and bWOA-V. They are used to decrease the complexity and increase the performance of a system by selecting significant features for classification purposes. The first bWOA-S version uses the Sigmoid transfer function to convert WOA values to binary ones, whereas the second bWOA-V version uses a hyperbolic tangent transfer function. Furthermore, the two binary variants introduced here were compared with three famous and well-known optimization algorithms in this domain, such as Particle Swarm Optimizer (PSO), three variants of binary ant lion (bALO1, bALO2, and bALO3), binary Dragonfly Algorithm (bDA) as well as the original WOA, over 24 benchmark datasets from the UCI repository. Eventually, a non-parametric test called Wilcoxon’s rank-sum was carried out at 5% significance to prove the powerfulness and effectiveness of the two proposed algorithms when compared with other algorithms statistically. The qualitative and quantitative results showed that the two introduced variants in the FS domain are able to minimize the selected feature number as well as maximize the accuracy of the classification within an appropriate time.

中文翻译：

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降维二进制鲸鱼优化算法

特征选择（FS）被视为全局组合优化问题。FS用于通过选择突出特征并删除不相关和冗余的数据来提供良好的分类结果，从而简化和提高高维数据集的质量。FS旨在减少尺寸并提高分类精度，而分类精度通常在模式分类，数据分析和数据挖掘应用等不同领域中非常重要。主要问题是找到包含所有数据代表信息的最佳子集。为了克服这个问题，提出了鲸鱼优化算法（WOA）的两个二进制变体，分别称为bWOA-S和bWOA-V。通过选择用于分类目的的重要功能，它们可用于降低复杂性并提高系统性能。第一个bWOA-S版本使用Sigmoid传递函数将WOA值转换为二进制值，而第二个bWOA-V版本使用双曲线正切传递函数。此外，将此处介绍的两个二进制变体与该领域的三种著名的优化算法进行了比较，例如粒子群优化器（PSO），二进制蚂蚁的三种变体（bALO1，bALO2和bALO3），二进制Dragonfly算法（bDA）以及原始WOA，以及来自UCI存储库的24个基准数据集。最终，我们以5％的显着性进行了一项名为Wilcoxon秩和的非参数检验，以证明在统计上与其他算法相比，这两种算法的功能和有效性。定性和定量结果表明，在FS域中引入的两个变体能够在适当的时间内使所选特征数最小化，并使分类的准确性最大化。