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:Department of Computer Science, Xiamen University, Xiamen, Fujian 361000, China.
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.
