A Classification Method for Microarrays Based on Diversity

Author(s): Xubo Wang, Xiangxiang Zeng, Ying Ju, Yi Jiang, Zhujin Zhang, Wenqiang Chen

Journal Name: Current Bioinformatics

Volume 11 , Issue 5 , 2016

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Graphical Abstract:


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.

Keywords: Gene expression data, feature selection, selective ensemble learning, clustering, diversity.

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Article Details

Year: 2016
Published on: 31 October, 2016
Page: [590 - 597]
Pages: 8
DOI: 10.2174/1574893609666140820224436
Price: $65

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