A novel scalable multi-class ROC for effective visualization and computation

Abstract. This paper introduces a new cost function for evaluating the multi-class classifier. The new cost function facilitates both a way to vi-sualize the performance (expected cost) of the multi-class classifier and a summary of the misclassification costs. This function overcomes the lim-itations of ROC in not being able to represent the classifier performance graphically when there are more than two classes. Here we present a new scalable method for producing a scalar measurement that is used to compare the performance of the multi-class classifier. We mathematically demonstrate that our technique can capture small variations in classifier performance