KDD-Cup 2000: question 1 winner's report
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A support vector method for multivariate performance measures
ICML '05 Proceedings of the 22nd international conference on Machine learning
Making the most of your data: KDD Cup 2007 "How Many Ratings" winner's report
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
A discriminative learning framework with pairwise constraints for video object classification
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Medical data mining: insights from winning two competitions
Data Mining and Knowledge Discovery
Tree induction over perennial objects
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Leakage in data mining: formulation, detection, and avoidance
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Design principles of massive, robust prediction systems
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Leakage in data mining: Formulation, detection, and avoidance
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
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We describe the ideas and methodologies that we developed in addressing the KDD Cup 2008 on early breast cancer detection, and discuss how they contributed to our success. The most important components of our solution were 1) the identification of predictive information in the patient identifier, 2) a linear SVM on the 117 provided features, and 3) a heuristic post-processing approach to optimize the evaluation criteria.