Cost-sensitive classifier evaluation using cost curves

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Proceedings titleAdvances in Knowledge Discovery and Data Mining: 12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings
Series titleLecture Notes in Computer Science; no. 5012
Conference12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, May 20-23, Osaka, Japan
AbstractThe 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.
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AffiliationNational Research Council Canada; NRC Institute for Information Technology
Peer reviewedYes
NPARC number23002093
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Record identifier437647c7-d5a9-4ade-843b-bd067de22e0c
Record created2017-08-14
Record modified2017-08-14
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