Local Characteristics of Minority Examples in Pre-processing of Imbalanced Data

摘要

Informed pre-processing methods for improving classifiers learned from class-imbalanced data are considered. We discuss different ways of analyzing the characteristics of local distributions of examples in such data. Then, we experimentally compare main informed preprocessing methods and show that identifying types of minority examples depending on their k nearest neighbourhood may help in explaining differences in performance of these methods. Finally, we exploit the information about the local neighbourhood to modify the oversampling ratio in a SMOTE-related method.