Abstract
We show that the Confusion Entropy, a measure of performance in multiclass problems has a strong (monotone) relation with the multiclass generalization of a classical metric, the Matthews Correlation Coefficient. Analytical results are provided for the limit cases of general no-information (n-face dice rolling) of the binary classification. Computational evidence supports the claim in the general case.
Publication types
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Comparative Study
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Research Support, Non-U.S. Gov't
MeSH terms
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Algorithms
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Area Under Curve
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Artificial Intelligence*
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Computational Biology / methods
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Computers
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Entropy
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Genotype
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Models, Statistical
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Oligonucleotide Array Sequence Analysis
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Probability
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ROC Curve
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Reproducibility of Results
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Software*
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Statistics as Topic*
Grants and funding
The authors acknowledge funding by the European Union FP7 Project HiperDART and by the PAT funded Project ENVIROCHANGE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.