Probabilistic Confusion Entropy for Evaluating Classifiers

Entropy

Xiao-Ning Wang²  Jin-Mao Wei¹  Han Jin²  Gang Yu² 

[1] College of Information Technical Science, Nankai University, Tianjin 300071, China;

关键词: confusion entropy;    probabilistic confusion entropy;    multi-class classification;   

DOI  :  10.3390/e15114969

来源: mdpi

【 摘 要 】

For evaluating the classification model of an information system, a proper measure is usually needed to determine if the model is appropriate for dealing with the specific domain task. Though many performance measures have been proposed, few measures were specially defined for multi-class problems, which tend to be more complicated than two-class problems, especially in addressing the issue of class discrimination power. Confusion entropy was proposed for evaluating classifiers in the multi-class case. Nevertheless, it makes no use of the probabilities of samples classified into different classes. In this paper, we propose to calculate confusion entropy based on a probabilistic confusion matrix. Besides inheriting the merit of measuring if a classifier can classify with high accuracy and class discrimination power, probabilistic confusion entropy also tends to measure if samples are classified into true classes and separated from others with high probabilities. Analysis and experimental comparisons show the feasibility of the simply improved measure and demonstrate that the measure does not stand or fall over the classifiers on different datasets in comparison with the compared measures.

【 授权许可】

CC BY   
© 2013 by the authors; licensee MDPI, Basel, Switzerland.