ClasSi: measuring ranking quality in the presence of object classes with similarity information
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
RadialViz: an orientation-free frequent pattern visualizer
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Hi-index | 0.00 |
This paper introduces a new cost function for evaluating the multi-class classifier. The new cost function facilitates both a way to visualize the performance (expected cost) of the multi-class classifier and a summary of the misclassification costs. This function overcomes the limitations 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.