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| A Survey of Evaluation and Design for AUC Based Classifier |
| WANG Yun-Yun, CHEN Song-Can |
| Department of Computer Science and Technology, College of Information Science and Technology Nanjing University of Aeronautics and Astronautics, Nanjing 210016 |
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Abstract Though as a common performance evaluating index for classification algorithms, accuracy (or total misclassification error) has several deficiencies, such as the sensitivity to class prior distribution and misclassification costs, and the ignorance of the posterior probability and ranking information obtained by classification algorithms. While the area under the receiver operation characteristic (ROC) curve measures the classification performance across the entire range of class prior distribution and misclassification costs, as well as the probability and ranking performance. Thus, it attracts much attention in classification learning and evokes a lot of researches. In this paper, a relative comprehensive survey for these researches is presented, including the advantages of AUC as a performance evaluating index, the design of algorithms based on AUC, the relationship between the accuracy-maximizing and AUC-maximizing algorithms and the deficiencies of AUC along with its variants.
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Received: 03 March 2010
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2011, Vol. 24 
