关键词: 不平衡分类, 预处理, 集成学习

Abstract: This paper studies the imbalanced data classification problem, and proposes two sampling methods for the imbalanced data classification. One is under-sampling by FarthestFirst clustering; the other is weighted random sampling. Both of them obtain better performance. Then this paper proposes a novel imbalanced data classification method, combining weighted random sampling with ensemble classifiers. In this method, the small samples are set various weights, and merged with large samples into new datasets. With a weighted random sampling for each new dataset, N balanced datasets can be got. These balanced datasets are trained with different classifiers, which will vote for the last result. Experiments show that this method has better classification performance.

Key words: imbalanced classification, preprocessing, ensemble learning