Chen Zhiqing, Xuan Ping, Heidari Ali Asghar, Liu Lei, Wu Chengwen, Chen Huiling, Escorcia-Gutierrez José, Mansour Romany F
School of Intelligent Manufacturing, Wenzhou Polytechnic, Wenzhou 325035, China.
Department of Computer Science, School of Engineering, Shantou University, Shantou 515063, China.
iScience. 2023 Apr 21;26(5):106679. doi: 10.1016/j.isci.2023.106679. eCollection 2023 May 19.
The domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.
当代医学和生物学领域已经产生了大量的高维遗传数据。识别代表性基因并降低数据维度可能具有挑战性。基因选择的目标是最小化计算成本并提高分类精度。因此，本文设计了一种新的包裹式基因选择算法，名为人工蜂群裸骨饥饿游戏搜索算法（ABHGS），它是将饥饿游戏搜索算法（HGS）与人工蜂群策略和高斯裸骨结构相结合来解决这个问题。为了评估和验证我们提出的方法的性能，在CEC 2017函数上，将ABHGS与HGS以及HGS中嵌入的单一策略、六种经典算法和十种先进算法进行了比较。实验结果表明，bABHGS优于原始的HGS。与同行相比，它提高了分类准确率并减少了所选特征的数量，表明其在空间搜索和特征选择方面具有实际工程效用。
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