Lin ZeSheng
Vocational Training Center, FoShan Open University, FoShan, Guangdong Province, China.
PLoS One. 2025 Jan 3;20(1):e0309741. doi: 10.1371/journal.pone.0309741. eCollection 2025.
Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention. However, traditional KELM algorithms have some problems when dealing with large-scale data, such as the need to adjust hyperparameters, poor interpretability, and low classification accuracy. To address these problems, this paper proposes an Enhanced Adaptive Whale Optimization Algorithm to optimize Kernel Extreme Learning Machine (EAWOA-KELM). Various methods were used to improve WOA. As a first step, a novel adaptive perturbation technique employing T-distribution is proposed to perturb the optimal position and avoid being trapped in a local maximum. Secondly, the WOA's position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. EAWOA demonstrates superior optimization accuracy compared to WOA across 21 test functions, with a notable edge on certain functions. The application of the upgraded WOA algorithm in KELM significantly improves the accuracy and efficiency of data classification by optimizing hyperparameters. This paper selects 7 datasets for classification experiments. Compared with the KELM optimized by WOA, the EAWOA optimized KELM in this paper has a significant improvement in performance, with a 5%-6% lead on some datasets, indicating the effectiveness of EAWOA-KELM in classification tasks.
数据分类是机器学习中的一个重要研究方向。为了有效处理海量数据集，研究人员引入了多种分类算法。值得注意的是，核极限学习机（KELM）作为一种快速有效的分类方法，受到了广泛关注。然而，传统的KELM算法在处理大规模数据时存在一些问题，如需要调整超参数、可解释性差和分类准确率低等。为了解决这些问题，本文提出了一种增强自适应鲸鱼优化算法来优化核极限学习机（EAWOA-KELM）。采用了多种方法对鲸鱼优化算法进行改进。第一步，提出了一种采用T分布的新型自适应扰动技术来扰动最优位置，避免陷入局部最大值。其次，通过引入惯性权重ω和增强收敛因子α对鲸鱼优化算法的位置更新公式进行了修改，从而提高了其局部搜索能力。此外，受灰狼优化算法的启发，使用3种优秀的粒子包围策略代替原来的随机选择粒子。最后，实现了一种新型的莱维飞行，以促进鲸鱼分布的多样性。实验结果表明，增强后的鲸鱼优化算法在适应度值和收敛速度方面均优于标准鲸鱼优化算法。在21个测试函数上，EAWOA的优化精度优于WOA，在某些函数上优势明显。升级后的鲸鱼优化算法在KELM中的应用通过优化超参数显著提高了数据分类的准确率和效率。本文选择了7个数据集进行分类实验。与用WOA优化的KELM相比，本文用EAWOA优化的KELM在性能上有显著提升，在某些数据集上领先5%-6%，表明EAWOA-KELM在分类任务中的有效性。
