基于邻域粗糙集与鱼群智能的基因选择方法

Gene Selection Method Based on Neighborhood Rough Sets and Fish Swarm Intelligence

针对高维、小样本及不确定性的基因表达数据,融合模糊可容忍性的邻域粒化技术与具有全局寻优能力的鱼群智能算法,提出基于邻域粗糙集与鱼群智能的基因选择方法。首先,采用邻域粗糙集对基因数据进行邻域粒化,形成邻域粒子;其次,提出基于邻域分类精度的不确定性评价函数,用以评价邻域粒子的不确定性,分辨关键性基因;进一步融合鱼群智能方法,设计一种基因选择算法,选取分类性强的少量关键基因;最后,在两个癌症基因数据集中进行基因选择,采用SVM分类器对获取的关键基因组进行分类实验。实验结果表明,采用该方法获取的基因组具有较低的冗余度及较好的分类性能。

Facing the gene expression data with high dimension, small samples and uncertainty, a gene selection method based on neighborhood rough sets and fish swarm intelligence is proposed by fusing a fuzzy tolerance granulation technology and a fish swarm intelligence algorithm with global optimization ability. Firstly, the neighborhood rough sets are used to granulate the gene data and form some neighborhood particles. Secondly, the neighborhood classification accuracy is presented as an uncertainty evaluation function that aims to judge these neighborhood particles and distinguish key genes. Furthermore, a gene selection algorithm based on artificial fish swarm intelligence is designed. Finally, some gene selection experiments are carried out on two tumor gene data sets. The classification experiments of a small number of selected key genes are conducted by using SVM classifier. The experimental results show that the genes selected by our proposed method have a low redundancy and a better classification performance.