Corpus ID: 218538125

Multi-Label Sampling based on Local Label Imbalance

@article{Liu2020MultiLabelSB,
  title={Multi-Label Sampling based on Local Label Imbalance},
  author={B. Liu and K. Blekas and Grigorios Tsoumakas},
  journal={ArXiv},
  year={2020},
  volume={abs/2005.03240}
}
  • B. Liu, K. Blekas, Grigorios Tsoumakas
  • Published 2020
  • Computer Science, Mathematics
  • ArXiv
  • Class imbalance is an inherent characteristic of multi-label data that hinders most multi-label learning methods. One efficient and flexible strategy to deal with this problem is to employ sampling techniques before training a multi-label learning model. Although existing multi-label sampling approaches alleviate the global imbalance of multi-label datasets, it is actually the imbalance level within the local neighbourhood of minority class examples that plays a key role in performance… CONTINUE READING

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