Recognition of Multiple Imbalanced Cancer Types Based on DNA Microarray Data Using Ensemble Classifiers

被引:12
|
作者
Yu, Hualong [1 ]
Hong, Shufang [1 ]
Yang, Xibei [1 ]
Ni, Jun [2 ]
Dan, Yuanyuan [3 ]
Qin, Bin [1 ]
机构
[1] Jiangsu Univ Sci & Technol, Sch Comp Sci & Engn, Zhenjiang 212003, Peoples R China
[2] Univ Iowa, Dept Radiol, Carver Coll Med, Iowa City, IA 52242 USA
[3] Jiangsu Univ Sci & Technol, Sch Biol & Chem Engn, Zhenjiang 212003, Peoples R China
基金
中国国家自然科学基金;
关键词
SUPPORT VECTOR MACHINES; GENE-EXPRESSION DATA; MOLECULAR CLASSIFICATION; REGULATORY NETWORK; CLASS PREDICTION; CARCINOMAS; BINARY;
D O I
10.1155/2013/239628
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
DNA microarray technology can measure the activities of tens of thousands of genes simultaneously, which provides an efficient way to diagnose cancer at the molecular level. Although this strategy has attracted significant research attention, most studies neglect an important problem, namely, that most DNA microarray datasets are skewed, which causes traditional learning algorithms to produce inaccurate results. Some studies have considered this problem, yet they merely focus on binary-class problem. In this paper, we dealt with multiclass imbalanced classification problem, as encountered in cancer DNA microarray, by using ensemble learning. We utilized one-against-all coding strategy to transform multiclass to multiple binary classes, each of them carrying out feature subspace, which is an evolving version of random subspace that generates multiple diverse training subsets. Next, we introduced one of two different correction technologies, namely, decision threshold adjustment or random undersampling, into each training subset to alleviate the damage of class imbalance. Specifically, support vector machine was used as base classifier, and a novel voting rule called counter voting was presented for making a final decision. Experimental results on eight skewed multiclass cancer microarray datasets indicate that unlike many traditional classification approaches, our methods are insensitive to class imbalance.
引用
收藏
页数:13
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