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Indexed by:期刊论文

Date of Publication:2017-08-01

Journal:COMPUTERS IN BIOLOGY AND MEDICINE

Included Journals:Scopus、SCIE、EI、PubMed

Volume:87

Page Number:8-21

ISSN No.:0010-4825

Key Words:Intersection neighborhood rough set; MapReduce; Affinity Propagation; Dynamic ensemble pruning; Microarray data

Abstract: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.