Imbalanced class problem; undersampling; membership probability; information loss
Issue Date
2020-01
Publisher
SPRINGER
Citation
JOURNAL OF CLASSIFICATION, v. 38, Issue 1, Page. 2-15
Abstract
Classifiers for a highly imbalanced dataset tend to bias in majority classes and, as a result, the minority class samples are usually misclassified as majority class. To overcome this, a proper undersampling technique that removes some majority samples can be an alternative. We propose an efficient and simple undersampling method for imbalanced datasets and show that the proposed method outperforms others with respect to four different performance measures by several illustrative experiments, especially for highly imbalanced datasets.