Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data.
Cancer classification from microarray gene expression data is one of the important areas of research in the field of computational biology and bioinformatics. Traditional supervised techniques often fail to produce desired accuracy as the number of clinically labeled patterns are very less. In such...
| Publicado en: | Journal of Biomedical Informatics Vol. 92; pp. 103136 - 103137 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Academic Press Inc.
Apr2019
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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=136179801&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136179801 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Apr2019 vid: 92 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 136179801 136179801 NLM30802546 10.1016/j.jbi.2019.103136 NLM30802546 136179801 ppf: 103136 ppct: 1 formats: tig: atl: Active learning using rough fuzzy classifier for cancer prediction from microarray gene expression data. aug: au: Halder, Anindya Kumar, Ansuman affil: Dept. of Computer Applications, North-Eastern Hill University, Tura Campus, Meghalaya 794002, India sug: subj: Gene Expression Profiling Methods Neoplasms Neoplasms Classification Logic Neoplasms Metabolism Algorithms Bioinformatics Resource Databases Scales ab: Cancer classification from microarray gene expression data is one of the important areas of research in the field of computational biology and bioinformatics. Traditional supervised techniques often fail to produce desired accuracy as the number of clinically labeled patterns are very less. In such situation, active learning technique can play an important role as it computationally selects only few most informative (confusing) samples to be labeled by the experts and are added to the training set which inturn can improve the accuracy of the prediction. In this work a novel active learning method using rough-fuzzy classifier (ALRFC) is proposed for cancer sample classification using gene expression data. The proposed technique can handle uncertainty, overlappingness, and indiscernibility usually present in the subtype classes of the gene expression data. The proposed algorithm is tested using different publicly available benchmark cancer datasets and the performance is compared of the proposed method with three other active learning techniques, one semi-supervised classification algorithm, and two (non-active) supervised counterpart learning techniques in terms of prediction accuracy, precision, recall, F1-measures and kappa. Superiority of the proposed method for cancer prediction over the other state-of-art techniques is established from the experimental results. Statistical significance of the better results achieved by the proposed method (in comparison to other methods) is also confirmed from the paired t-test results for most of the datasets. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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