The aim of the feature selection technique is to obtain the most important information from a specific set of datasets. Further elaborations in the feature selection technique will positively affect the classification process, which can be applied in various areas such as machine learning, pattern recognition, and signal processing. In this study, a hybrid algorithm between the binary dragonfly algorithm (BDA) and the statistical dependence (SD) is presented, whereby the feature selection method in discrete space is modeled as a binary-based optimization algorithm, guiding BDA and using the accuracy of the k-nearest neighbors classifier on the dataset to verify it in the chosen fitness function. The experimental results demonstrated that the proposed algorithm, which we refer to as SD-BDA, outperforms other algorithms in terms of the accuracy of the results represented by the cost of the calculations and the accuracy of the classification. KeywordsFeature selection; Classification; Dragonfly algorithm; Statistical dependence.

中文翻译：

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统计相关性的混合二进制蜻蜓优化算法用于特征选择

特征选择技术的目的是从一组特定的数据集中获取最重要的信息。特征选择技术的进一步细化将对分类过程产生积极影响，该分类过程可应用于机器学习，模式识别和信号处理等各个领域。该研究提出了一种二进制蜻蜓算法（BDA）与统计依赖关系（SD）之间的混合算法，从而将离散空间中的特征选择方法建模为基于二进制的优化算法，以指导BDA并利用其准确性。数据集中的k个最近邻分类器，以在选定的适应度函数中对其进行验证。实验结果表明，该算法被称为SD-BDA，在计算成本和分类精度方面，结果的准确性优于其他算法。特征选择; 分类; 蜻蜓算法；统计依赖性。