A Classification Method for Microarrays Based on Diversity

Title:A Classification Method for Microarrays Based on Diversity

Volume: 11 Issue: 5

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

Affiliation:

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

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.

Cite this article as:

Wang Xubo, Zeng Xiangxiang, Ju Ying, Jiang Yi, Zhang Zhujin and Chen Wenqiang, A Classification Method for Microarrays Based on Diversity, Current Bioinformatics 2016; 11 (5) . https://dx.doi.org/10.2174/1574893609666140820224436