Show simple item record

dc.contributor.authorPeng, Yonghong*
dc.date.accessioned2009-10-20T06:30:38Z
dc.date.available2009-10-20T06:30:38Z
dc.date.issued2006
dc.identifier.citationPeng, Y. (2006). A Novel Ensemble Machine Learning for Robust Microarray Data Classification. Computers in Biology and Medicine. Vol. 36, No. 6, pp. 553-573.en
dc.identifier.urihttp://hdl.handle.net/10454/3688
dc.descriptionNoen
dc.description.abstractMicroarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).en
dc.language.isoenen
dc.relation.isreferencedbyhttp://dx.doi.org/10.1016/j.compbiomed.2005.04.001en
dc.subjectMicroarray dataen
dc.subjectMachine learningen
dc.subjectEnsemble learningen
dc.subjectClassificationen
dc.titleA Novel Ensemble Machine Learning for Robust Microarray Data Classification.en
dc.status.refereedYesen
dc.typeArticleen
dc.type.versionNo full-text available in the repositoryen


This item appears in the following Collection(s)

Show simple item record