C5.0 specifications


Unique identifier OMICS_30406
Name C5.0
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Kristin Nicodemus

Publication for C5.0

C5.0 citations


Development of machine learning models for diagnosis of glaucoma

PLoS One
PMCID: 5441603
PMID: 28542342
DOI: 10.1371/journal.pone.0177726

[…] ediction result. Therefore, they are not entirely suitable for medical diagnosis because clinicians want to know both the prediction and the reason for the prediction. Decision tree models [] such as C5.0 [,] show good interpretability and poor prediction power. Logistic Regression and Naïve Bayes are algorithms used for probabilistic classification []. iDHS-EL [] and iRSpot-EL [] are predictors d […]


Using Hierarchical Time Series Clustering Algorithm and Wavelet Classifier for Biometric Voice Classification

PMCID: 3351073
PMID: 22619492
DOI: 10.1155/2012/215019

[…] a new voiceprint is received, pass it over the rules by checking its coefficient values that can determine which class label the voiceprint fits in. The other decision tree algorithm is the classical C5.0 or J48 with pruning mode on, in WEKA which is an open source of machine learning algorithms for solving data mining problems implemented in Java and open sourced under the GPL (http://archive.ics […]

C5.0 institution(s)
Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK; Centre for Cognitive Ageing and Cognitive Epidemiology, University of Edinburgh, Edinburgh, UK; Smurfit Institute of Genetics and Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland; MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK; Department of Psychology, University of Edinburgh, Edinburgh, UK
C5.0 funding source(s)
Supported by Rosetrees Trust Biomedical Research Grant M405, the Irish Research Council GOPIG/2013/763, a University of Edinburgh Chancellor’s Fellowship, the Centre for Cognitive Ageing and Cognitive Epidemiology.

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