Journal
BIOINFORMATICS
Volume 31, Issue 11, Pages 1851-1853Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btv066
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Funding
- ARC Early Career Award [DE130101670]
- ARC Future Fellowship [FT0991918]
- Australian Postgraduate Award
- Australian Research Council [DE130101670] Funding Source: Australian Research Council
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Although a large collection of classification software packages exist in R, a new generic framework for linking custom classification functions with classification performance measures is needed. A generic classification framework has been designed and implemented as an R package in an object oriented style. Its design places emphasis on parallel processing, reproducibility and extensibility. Finally, a comprehensive set of performance measures are available to ease post-processing. Taken together, these important characteristics enable rapid and reproducible benchmarking of alternative classifiers.
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