期刊
CURRENT BIOINFORMATICS
卷 11, 期 5, 页码 590-597出版社
BENTHAM SCIENCE PUBL LTD
DOI: 10.2174/1574893609666140820224436
关键词
Gene expression data; feature selection; selective ensemble learning; clustering; diversity
资金
- National Natural Science Foundation of China [61202011, 61272385, 61272152, 61071151]
- China Postdoctoral Science Foundation [2012M511485, 2013T60374]
Background: Analysis on classification of microarray gene expression data has been an important research topic in bioinformatics. Objective: For the unsatisfied performance of basic classification methods, researches on ensemble classifiers prove ensembling classifiers to be an efficient way to increase classification accuracy. Method: In this paper, we propose a new diversity-based classification method, which combines a feature selection method based on clustering and an ensemble classifier D3C to improve the classification accuracy. D3C is a novel ensemble method which utilizes ensemble pruning based on k-means clustering and dynamic selection and circulating combination aiming at obtaining diversity among classifiers. Results & Conclusion: We apply our proposed method on seven gene data sets. Compared to prior research, experimental results reveal that our method outperforms other ensemble classifiers in accuracy for gene classification.
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