A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

被引：217
|
作者：
Jurman, Giuseppe [1 ]
Riccadonna, Samantha [1 ]
Furlanello, Cesare [1 ]
机构：
[1] Fdn Bruno Kessler, Trento, Italy
来源：
PLOS ONE | 2012年 / 7卷 / 08期
关键词：
ROC CURVE; STATISTICAL COMPARISONS; AREA; PERFORMANCE; CLASSIFIERS; CLASSIFICATION; ACCURACY; AUC;
D O I：
10.1371/journal.pone.0041882
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
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页数：8
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