An Adaptive Sampling Ensemble Classifier for Learning from Imbalanced Data Sets

作者：Geiler Ordonez Jon^*; Hong Li; Yue Jian Guo

来源：International Multi-Conference of Engineers and Computer Scientists 2010, 2010-03-17 to 2010-03-19.

• Data Mining
• Ensemble algorithm
• Imbalanced data sets
• Synthetic Samples

In Imbalanced datasets, minority classes can be erroneously classified by common classification algorithms. In this paper, an ensemble-base algorithm is proposed by creating new balanced training sets with all the minority class and under-sampling majority class. In each round, algorithm identified hard examples on majority class and generated synthetic examples for the next round. For each training set a Weak Learner is used as base classifier. Final predictions would be achieved by casting a majority vote. This method is compared whit some known algorithms and experimental results demonstrate the effectiveness of the proposed algorithm.

• 出版日期2010
• 单位中南大学