Selective voting in convex-hull ensembles improves classification accuracy.
Objective: Classification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routin...
| Publicado en: | Artificial Intelligence in Medicine Vol. 54; no. 3; pp. 171 - 180 |
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| Autores principales: | , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Mar2012
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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=104536080&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104536080 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09333657 3HY jtl: Artificial Intelligence in Medicine issn: 09333657 maglogo: N pubinfo: dt: Mar2012 vid: 54 iid: 3 pid: 1004 pub: Elsevier B.V. artinfo: ui: 104536080 NLM22064044 2011492912 10.1016/j.artmed.2011.10.003 NLM22064044 PMC3666100 104536080 ppf: 171 ppct: 9 formats: tig: atl: Selective voting in convex-hull ensembles improves classification accuracy. aug: au: Kodell RL Zhang C Siegel ER Nagarajan R Kodell, Ralph L Zhang, Chuanlei Siegel, Eric R Nagarajan, Radhakrishnan affil: Department of Biostatistics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, United States sug: subj: Algorithms Classification Methods Early Detection of Cancer Disease Susceptibility Classification Human Risk Assessment ab: Objective: Classification algorithms can be used to predict risks and responses of patients based on genomic and other high-dimensional data. While there is optimism for using these algorithms to improve the treatment of diseases, they have yet to demonstrate sufficient predictive ability for routine clinical practice. They generally classify all patients according to the same criteria, under an implicit assumption of population homogeneity. The objective here is to allow for population heterogeneity, possibly unrecognized, in order to increase classification accuracy and further the goal of tailoring therapies on an individualized basis.Methods and Materials: A new selective-voting algorithm is developed in the context of a classifier ensemble of two-dimensional convex hulls of positive and negative training samples. Individual classifiers in the ensemble are allowed to vote on test samples only if those samples are located within or behind pruned convex hulls of training samples that define the classifiers.Results: Validation of the new algorithm's increased accuracy is carried out using two publicly available datasets having cancer as the outcome variable and expression levels of thousands of genes as predictors. Selective voting leads to statistically significant increases in accuracy from 86.0% to 89.8% (p<0.001) and 63.2% to 67.8% (p<0.003) compared to the original algorithm.Conclusion: Selective voting by members of convex-hull classifier ensembles significantly increases classification accuracy compared to one-size-fits-all approaches. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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