A comparison of MCC and CEN error measures in multi-class prediction.
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.
Fondazione Bruno Kessler, Trento, Italy. jurman@fbk.eu
Giuseppe
G
Jurman
Samantha
S
Riccadonna
Cesare
C
Furlanello
D000465:Algorithms; D019540:Area Under Curve; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D003201:Computers; D019277:Entropy; D005838:Genotype; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011336:Probability; D012372:ROC Curve; D015203:Reproducibility of Results; D012984:Software; D013223:Statistics as Topic