Feature selection, an optimization problem, becomes an important pre-process tool in data mining, which simultaneously aims at minimizing feature-size and maximizing model generalization. Because of large search space, conventional optimization methods often fail to generate global optimum solution. A variety of hybrid techniques merging different search strategies have been proposed in feature selection literature, but mostly deal with low dimensional datasets. In this paper, a hybrid optimization method is proposed for numerical optimization and feature selection, which integrates sine-cosine algorithm (SCA) in Harris hawks optimization (HHO). The goal of SCA integration is to cater ineffective exploration in HHO, moreover exploitation is enhanced by dynamically adjusting candidate solutions for avoiding solution stagnancy in HHO. The proposed method, namely SCHHO, is evaluated by employing CEC'17 test suite for numerical optimization and sixteen datasets with low and high-dimensions exceeding 15000 attributes, and compared with original SCA and HHO, as well as, other well-known optimization methods like dragony algorithm (DA), whale optimization algorithm (WOA), grasshopper optimization algorithm (GOA), Grey wolf optimization (GWO), and salp swarm algorithm (SSA); in addition to state-of-the-art methods. Performance of the proposed method is also validated against hybrid methods proposed in recent related literature. The extensive experimental and statistical analyses suggest that the proposed hybrid variant of HHO is able to produce effcient search results without additional computational cost. With increased convergence speed, SCHHO reduced feature-size up to 87% and achieved accuracy up to 92%. Motivated from the findings of this study, various potential future directions are also highlighted.

特征选择是一个优化问题，已成为数据挖掘中的重要预处理工具，同时旨在最小化特征大小和最大化模型泛化。由于搜索空间大，传统的优化方法通常无法生成全局最优解。在特征选择文献中已经提出了各种融合了不同搜索策略的混合技术，但是大多数涉及低维数据集。本文提出了一种用于数值优化和特征选择的混合优化方法，该方法将正弦余弦算法（SCA）集成到了Harris hawks优化（HHO）中。SCA集成的目标是解决HHO中无效的勘探问题，而且通过动态调整候选解决方案来避免HHO中的解决方案停滞，从而提高了开发效率。通过使用CEC'17测试套件进行数值优化以及16个低维和高维数据集超过15000个属性的数据集，对所提出的方法SCHHO进行了评估，并将其与原始SCA和HHO以及其他众所周知的优化方法进行了比较如Dragony算法（DA），鲸鱼优化算法（WOA），蚱hopper优化算法（GOA），灰狼优化（GWO）和salp swarm算法（SSA）；除了最新的方法。相对于最近相关文献中提出的混合方法，也验证了该方法的性能。大量的实验和统计分析表明，所提出的HHO混合变体能够产生有效的搜索结果，而无需额外的计算成本。随着收敛速度的提高，SCHHO将特征尺寸减小了多达87％，将精度减小了多达92％。从这项研究的发现出发，还强调了各种潜在的未来方向。