Information Technology Department, College of Computer and Information Sciences, King Saud University (KSU), Riyadh 11451, Saudi Arabia.
Sensors (Basel). 2022 Sep 26;22(19):7273. doi: 10.3390/s22197273.
This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with -nearest neighbors (-NN). In both algorithms, the goal is to determine a small gene subset that can be used to classify samples with a high degree of accuracy. The proposed algorithms are divided into two phases. To obtain an accurate gene set and to deal with the challenge of high-dimensional data, the redundancy analysis and relevance calculation are conducted in the first phase. To solve the gene selection problem, the second phase applies SVM and -NN with leave-one-out cross-validation. A performance evaluation was performed on six microarray data sets using the two proposed algorithms. A comparison of the two proposed algorithms with several known algorithms indicates that both of them perform quite well in terms of classification accuracy and the number of selected genes.
本文提出了两种用于基因选择的新型群体智能算法，即 HHO-SVM 和 HHO-KNN。这两种算法都基于哈里斯鹰优化（HHO），一种与支持向量机（SVM）结合，另一种与 K-最近邻（K-NN）结合。在这两种算法中，目标都是确定一个可以用于高度准确地分类样本的小基因子集。所提出的算法分为两个阶段。为了获得准确的基因集并应对高维数据的挑战，在第一阶段进行了冗余分析和相关性计算。为了解决基因选择问题，第二阶段使用 SVM 和 K-NN 并结合留一交叉验证。在六个微阵列数据集上对这两种算法进行了性能评估。与几种已知算法的比较表明，这两种算法在分类准确性和选择的基因数量方面都表现得相当好。
