Quantum-Inspired Owl Search Algorithm with Ensembles of Filter Methods for Gene Subset Selection from Microarray Data
Quantum-Inspired Owl Search Algorithm with Ensembles of Filter Methods for Gene Subset Selection from Microarray Data
复制标题
量子启发的 Owl 搜索算法与过滤方法集合，用于从微阵列数据中选择基因子集
DOI：
10.1142/s0218001423510011
复制
发表时间：
2023
影响因子：
1.5
通讯作者：
Chakraborty Basabi
中科院分区：
文献类型：
--
作者：
Mandal Ashis Kumar;Sen Rikta;Chakraborty Basabi
由于微阵列数据通常是高维的，并且包含许多不相关的和冗余的基因，因此寻找用于微阵列分类的基因的最佳子集是费力的。为了克服这个问题，我们提出了一个两步技术。在第一步中，为了减少大量的基因或特征，使用具有过滤器评估度量的流行的基于排名的特征选择算法的集合来选择一组排名靠前的基因。在下一步中，量子启发的猫头鹰搜索算法（），一种新的基于过滤器适应度函数的元启发式搜索技术，结合了量子计算的概念，被开发用于从预定列表中识别最佳基因子集。实验结果表明，集成方法在第一步中可以选择更多的显性基因组比每个单独的过滤器。此外，它已被发现，可以减少所选择的最佳基因子集的基数具有可比的分类精度，并需要更少的计算时间比我们早先提出的基于QIOSA的包装方法（即）。此外，与三种流行的进化特征子集选择算法相比，在保持可接受的分类精度的同时，有效地减少了基因子集的最优基数。
Finding the optimum subset of genes for microarray classification is laborious because microarray data are often high-dimensional and contain many irrelevant and redundant genes. To overcome this problem, we have proposed a two-step technique. In the first step, to reduce the vast number of genes or features, an ensemble of popular rank-based feature selection algorithms with filter evaluation metrics are used to select a group of top-ranking genes. In the next step, the quantum-inspired owl search algorithm (), a new filter fitness function-based metaheuristic search technique incorporating concepts from quantum computing, is developed to identify the best subset of genes from the predetermined list. The experimental findings reveal that the ensemble approach in the first step can select more dominant groups of genes than each of the individual filters. Furthermore, it has been found thatcan reduce the cardinality of the selected optimum gene subset with comparable classification accuracy and requires lesser computational time than our earlier proposed QIOSA-based wrapper approach (i.e.). Besides, compared with three popular evolutionary feature subset selection algorithms,efficiently reduces the optimum cardinality of the gene subset while maintaining acceptable classification accuracy.