National Research Council of Canada. NRC Institute for Information Technology
12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 20-23, Osaka, Japan
The evaluation of classifier performance in a cost-sensitive setting is straightforward if the operating conditions (misclassification costs and class distributions) are fixed and known. When this is not the case, evaluation requires a method of visualizing classifier performance across the full range of possible operating conditions. This talk outlines the most important requirements for cost-sensitive classifier evaluation for machine learning and KDD researchers and practitioners, and introduces a recently developed technique for classifier performance visualization – the cost curve – that meets all these requirements.
Advances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings: 26–29.