Best First Over-Sampling for Multilabel Classification
Xusheng Ai, Jian Wu, Victor S. Sheng, Yufeng Yao, Pengpeng Zhao, Zhiming Cui · 2015
Learning from imbalanced multilabel data is a challenging task. It has attracted considerable attention recently. In this paper we propose a MultiLabel Best First Over-sampling (ML-BFO) to improve the performance of multilabel classification algorithms, based on imbalance minimization and Wilson's ENN rule. Our experimental results show that ML-BFO not only duplicates fewer samples but also reduces the imbalance level much more than two state-of-the-art multilabel sampling methods, i.e., an over-sampling method LP-ROS and an under-sampling method MLeNN. Besides, ML-BFO significantly improves the performance of multilabel classification algorithms, and performs much better than LP-ROS and MLeNN.