학술논문
Impact studies and sensitivity analysis in medical data mining with ROC-based genetic learning
Document Type
Conference
Source
Third IEEE International Conference on Data Mining Data mining Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. :637-640 2003
Subject
Language
Abstract
ROC curves have been used for a fair comparison of machine learning algorithms since the late 90's. Accordingly, the area under the ROC curve (AUC) is nowadays considered a relevant learning criterion, accommodating imbalanced data, misclassification costs and noisy data. We show how a genetic algorithm-based optimization of the AUC criterion can be exploited for impact studies and sensitivity analysis. The approach is illustrated on the Atherosclerosis Identification problem, PKDD 2002 Challenge.