Rank Pruning Approach for Noisy Multi-label Learning
Si-ming Lian, Jian–wei Liu, Run‐kun Lu, Xionglin Luo · 2018
Currently, the research point of multi-label learning has shifted to a new direction, that is, Noisy Multi-label learning. How to solve such problem that the training examples with the flipped labels, and make full use of the information of the true label sets to improve the performance of noisy multi-label learning is a challenge. In this paper, we leverage the rank pruning to process the noisy multi-label problem called Rank Pruning Approach for Noisy Multi-Label Classification (RPANMLC). Different from the previous approaches, we adopt the rank pruning to remove the unconfident samples and estimate the noisy label rates. By this way, the original real labels have been preserved, which can improve the performance of the learning algorithm. Another obvious advantage of this approach is that we spend most time to train the "confident" samples rather than the whole corrupted samples. Besides, we also give the upper regret bound of the RPANMLC. Furthermore, other robust noisy multi-label learning methods have been compared with the RPANMLC, and experimental results on four real world data sets verify the effectiveness of our proposed algorithms.