The process of dimensionality reduction is a crucial solution to deal with the dimensionality problem that may be faced when dealing with the majority of machine learning techniques. This paper proposes an enhanced hybrid metaheuristic approach using grey wolf optimizer (GWO) and whale optimization algorithm (WOA) to develop a wrapper-based feature selection method. The main objective of the proposed technique is to alleviate the drawbacks of both algorithms, including immature convergence and stagnation to local optima (LO). The hybridization is done with improvements in the mechanisms of both algorithms. To confirm the stability of the proposed approach, 18 well-known datasets are employed from the UCI repository. Furthermore, the classification accuracy, number of selected features, fitness values, and run time matrices are collected and compared with a set of well-known feature selection approaches in the literature. The results show the superiority of the proposed approach compared with both GWO and WOA. The results also show that the proposed hybrid technique outperforms other state-of-the-art approaches, significantly.

中文翻译：

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高效的混合自然启发式二元优化器，用于特征选择

降维过程是解决大多数机器学习技术可能面临的维数问题的关键解决方案。本文提出了一种增强的混合元启发式方法，该方法使用灰狼优化器（GWO）和鲸鱼优化算法（WOA）来开发基于包装器的特征选择方法。所提出的技术的主要目的是减轻两种算法的缺点，包括不成熟的收敛和停滞到局部最优（LO）。通过改进两种算法的机制来完成杂交。为了确认所提出方法的稳定性，从UCI存储库中采用了18个著名的数据集。此外，分类的准确性，所选特征的数量，适合度值，收集运行时间矩阵，并将其与文献中的一组众所周知的特征选择方法进行比较。结果表明，与GWO和WOA相比，该方法具有优越性。结果还表明，提出的混合技术明显优于其他最新技术。