Class-Imbalanced Complementary-Label Learning via Weighted Loss
- URL: http://arxiv.org/abs/2209.14189v2
- Date: Sat, 17 Jun 2023 08:03:06 GMT
- Title: Class-Imbalanced Complementary-Label Learning via Weighted Loss
- Authors: Meng Wei, Yong Zhou, Zhongnian Li, Xinzheng Xu
- Abstract summary: Complementary-label learning (CLL) is widely used in weakly supervised classification.
It faces a significant challenge in real-world datasets when confronted with class-imbalanced training samples.
We propose a novel problem setting that enables learning from class-imbalanced complementary labels for multi-class classification.
- Score: 8.934943507699131
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Complementary-label learning (CLL) is widely used in weakly supervised
classification, but it faces a significant challenge in real-world datasets
when confronted with class-imbalanced training samples. In such scenarios, the
number of samples in one class is considerably lower than in other classes,
which consequently leads to a decline in the accuracy of predictions.
Unfortunately, existing CLL approaches have not investigate this problem. To
alleviate this challenge, we propose a novel problem setting that enables
learning from class-imbalanced complementary labels for multi-class
classification. To tackle this problem, we propose a novel CLL approach called
Weighted Complementary-Label Learning (WCLL). The proposed method models a
weighted empirical risk minimization loss by utilizing the class-imbalanced
complementary labels, which is also applicable to multi-class imbalanced
training samples. Furthermore, we derive an estimation error bound to provide
theoretical assurance. To evaluate our approach, we conduct extensive
experiments on several widely-used benchmark datasets and a real-world dataset,
and compare our method with existing state-of-the-art methods. The proposed
approach shows significant improvement in these datasets, even in the case of
multiple class-imbalanced scenarios. Notably, the proposed method not only
utilizes complementary labels to train a classifier but also solves the problem
of class imbalance.
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