ACM SIGKDD Explorations Newsletter · 2000 · 216 citations · 0 references
ComputingComputer ScienceData ScienceData MiningMachine LearningStatistical LearningClassificationDecision TreesEnsemble LearningEnsemble MethodsPattern RecognitionRepresentation LearningBaggingClass Imbalance LearningClassification LossConditional Risk EstimationEnsemble SamplingImbalanced ClassificationNaive BayesSampling
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.