New perspective on the classification accuracy based on neighborhood system
Shuang Liu, Chengcheng Qian · International Journal of Knowledge-based and Intelligent Engineering Systems · 2017
Classification accuracy plays an important role in the evaluation of single-label and multi-label classification methods. Many different evaluation methods have been proposed to evaluate different kind of classification methods. Even in the same kind of classification methods, there are also many different evaluation methods. In this paper, we seek to present a unified evaluation criterion for the classification accuracy measurement, irrespective of single-label and multi-label classification method. We use neighborhood system theory to design absolute accuracy (AA) and relative accuracy rate (RAR) to evaluate different single-label classification methods, then apply them to evaluate different multi-label classification methods. And finally, some important examples are illustrated to understand the unified evaluation criterion in different classification situations.