福州大学学报（自然科学版）2017，Vol.45Issue(6)：815-821,7.DOI:10.7631/issn.1000-2243.2017.06.0815
基于邻域熵与蚁群优化的基因选择算法
Gene selection algorithm based on neighborhood entropy and ant colony optimization
摘要
Abstract
To deal with the problem of selecting gene subset in a gene dataset, a novel gene selection method is proposed. Firstly, a neighborhood rough set model is introduced to granulate the gene data. The neighborhood entropy is defined for measuring the uncertainty of gene data and removing the redundant genes to constitute a pre-selected subset. Furthermore, the neighborhood entropy based gene importance is constructed as the heuristic information in the proposed ACO algorithm, which has the advantages of distributed, positive feedback and global optimization. The proposed algorithm has a good ability for finding the minimum critical gene subset from the pre-selected set. Finally, the classi-fication experiments are carried out on the selected genes. The results show that the classifier construc-ted on the selected genes has a good classification performance.关键词
基因选择/蚁群优化/邻域熵/邻域粗糙集Key words
gene selection/ant colony optimization/neighborhood entropy/neighborhood rough sets分类
交通工程引用本文复制引用
许明,郑鹭斌,谢彦麒,陈玉明..基于邻域熵与蚁群优化的基因选择算法[J].福州大学学报（自然科学版）,2017,45(6):815-821,7.基金项目
国家自然科学基金资助项目( 61573297) （ 61573297）
福建省教育厅科研资助项目( JA15363) （ JA15363）
