Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning

I
ICDM
2003
Third IEEE International Conference on Data Mining (ICDM'03)
Abstract - Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning

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	Similar Articles Articles by Mich?le Sebag Articles by J?r? Az? Articles by No? Lucas

Third IEEE International Conference on Data Mining (ICDM'03)

Melbourne, Florida

November 19-November 22

ISBN: 0-7695-1978-4

	ASCII Text	x
	Mich?le Sebag, J?r? Az?, No? Lucas, "Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning," Data Mining, IEEE International Conference on, pp. 637, Third IEEE International Conference on Data Mining (ICDM'03), 2003.

	BibTex	x
	@article{ 10.1109/ICDM.2003.1250996, author = {Mich?le Sebag and J?r? Az? and No? Lucas}, title = {Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2003}, isbn = {0-7695-1978-4}, pages = {637}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250996}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning SN - 0-7695-1978-4 SP EP A1 - Mich?le Sebag, A1 - J?r? Az?, A1 - No? Lucas, PY - 2003 KW - null VL - 0 JA - Data Mining, IEEE International Conference on ER -

Mich?le Sebag, Universit? Paris-Sud Orsay, France

J?r? Az?, Universit? Paris-Sud Orsay, France

No? Lucas, Universit? Paris-Sud Orsay, France

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2003.1250996

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.

This paper shows 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.

Citation:

Mich?le Sebag, J?r? Az?, No? Lucas, "Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning," icdm, pp.637, Third IEEE International Conference on Data Mining (ICDM'03), 2003

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