Dealing with Imbalanced Dataset: A Re-sampling Method Based on the Improved SMOTE Algorithm

摘要

Most classification models have presented an imbalanced learning state when dealing with the imbalanced datasets. This article proposes a novel approach for learning from imbalanced datasets, which based on an improved SMOTE (synthetic Minority Over-sampling technique) algorithm. By organically combining the over-sampling and the under-sampling method, this approach aims to choose neighbors targetedly and synthesize samples with different strategy. Experiments show that most classifiers have achieved an ideal performance on the classification problem of the positive and negative class after dealing imbalanced datasets with our algorithm.