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Impact studies and sensitivity analysis in medical data mining with ROC-based genetic learning

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3 Author(s)
Sebag, M. ; PCRI, Univ. Paris, Orsay, France ; Aze, J. ; Lucas, N.

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.

Published in:

Data Mining, 2003. ICDM 2003. Third IEEE International Conference on

Date of Conference:

19-22 Nov. 2003