Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers.
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 m...
| Publicado en: | BioMed Research International Vol. 2013; pp. 239628 - 239629 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Wiley-Blackwell
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104101189&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104101189 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 2013 vid: 2013 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 104101189 2012330600 NLM24078908 PMC3770038 104101189 ppf: 239628 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Recognition of multiple imbalanced cancer types based on DNA microarray data using ensemble classifiers. aug: au: Yu, Hualong Hong, Shufang Yang, Xibei Ni, Jun Dan, Yuanyuan Qin, Bin affil: School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China. sug: subj: Algorithms Neoplasms Oligonucleotide Array Sequence Analysis Statistics Databases ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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