Winning the KDD99 classification cup 论文
2000ACM SIGKDD Explorations Newsletter引用 216
Machine Learning and Data ClassificationAdvanced Statistical Methods and ModelsImbalanced Data Classification Techniques
摘要
We briefly describe our approach for the KDD99 Classification Cup. The solution is essentially a mixture of bagging and boosting. Additionally, asymmetric error costs are taken into account by minimizing the so-called conditional risk . Furthermore, the standard sampling with replacement methodology of bagging was modified to put a specific focus on the smaller but expensive-if-predicted-wrongly classes.