Dataset Information

Selective voting in convex-hull ensembles improves classification accuracy.

ABSTRACT: 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.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.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.Selective voting by members of convex-hull classifier ensembles significantly increases classification accuracy compared to one-size-fits-all approaches.

SUBMITTER: Kodell RL

PROVIDER: S-EPMC3666100 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature

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Selective voting in convex-hull ensembles improves classification accuracy.

Kodell Ralph L RL Zhang Chuanlei C Siegel Eric R ER Nagarajan Radhakrishnan R

Artificial intelligence in medicine 20111106 3

<h4>Objective</h4>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 h ...[more]

PMID: 22064044

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Project description:The external appearance of the dodo (Raphus cucullatus, Linnaeus, 1758) has been a source of considerable intrigue, as contemporaneous accounts or depictions are rare. The body mass of the dodo has been particularly contentious, with the flightless pigeon alternatively reconstructed as slim or fat depending upon the skeletal metric used as the basis for mass prediction. Resolving this dichotomy and obtaining a reliable estimate for mass is essential before future analyses regarding dodo life history, physiology or biomechanics can be conducted. Previous mass estimates of the dodo have relied upon predictive equations based upon hind limb dimensions of extant pigeons. Yet the hind limb proportions of dodo have been found to differ considerably from those of their modern relatives, particularly with regards to midshaft diameter. Therefore, application of predictive equations to unusually robust fossil skeletal elements may bias mass estimates. We present a whole-body computed tomography (CT) -based mass estimation technique for application to the dodo. We generate 3D volumetric renders of the articulated skeletons of 20 species of extant pigeons, and wrap minimum-fit 'convex hulls' around their bony extremities. Convex hull volume is subsequently regressed against mass to generate predictive models based upon whole skeletons. Our best-performing predictive model is characterized by high correlation coefficients and low mean squared error (a = - 2.31, b = 0.90, r (2) = 0.97, MSE = 0.0046). When applied to articulated composite skeletons of the dodo (National Museums Scotland, NMS.Z.1993.13; Natural History Museum, NHMUK A.9040 and S/1988.50.1), we estimate eviscerated body masses of 8-10.8 kg. When accounting for missing soft tissues, this may equate to live masses of 10.6-14.3 kg. Mass predictions presented here overlap at the lower end of those previously published, and support recent suggestions of a relatively slim dodo. CT-based reconstructions provide a means of objectively estimating mass and body segment properties of extinct species using whole articulated skeletons.

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Selective voting in convex-hull ensembles improves classification accuracy.

Publications

Selective voting in convex-hull ensembles improves classification accuracy.

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