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论文类型：期刊论文

发表时间：2017-08-01

发表刊物：COMPUTERS IN BIOLOGY AND MEDICINE

收录刊物：Scopus、SCIE、EI、PubMed

卷号：87

页面范围：8-21

ISSN号：0010-4825

关键字：Intersection neighborhood rough set; MapReduce; Affinity Propagation; Dynamic ensemble pruning; Microarray data

摘要：Gene selection and sample classification based on gene expression data are important research areas in bioinformatics. Selecting important genes closely related to classification is a challenging task due to high dimensionality and small sample size of microarray data. Extended rough set based on neighborhood has been successfully applied to gene selection, as it can select attributes without redundancy and deal with numerical attributes directly. However, the computation of approximations in rough set is extremely time consuming. In this paper, in order to accelerate the process of gene selection, a parallel computation method is proposed to calculate approximations of intersection neighborhood rough set. Furthermore, a novel dynamic ensemble pruning approach based on Affinity Propagation clustering and dynamic pruning framework is proposed to reduce memory usage and computational cost. Experimental results on three Arabidopsis thaliana biotic and abiotic stress response datasets demonstrate that the proposed method can obtain better classification performance than ensemble method with gene pre-selection.