期刊
CMC-COMPUTERS MATERIALS & CONTINUA
卷 72, 期 3, 页码 5607-5628出版社
TECH SCIENCE PRESS
DOI: 10.32604/cmc.2022.028154
关键词
Semi-supervised learning; class imbalance; iterative learning; unlabeled data
资金
- National Research Foundation of Korea [2020R1A2C1014829]
- Korea Medical Device Development Fund grant - Government of the Republic of Korea Korea government (the Ministry of Science and ICT)
- Ministry of Trade, Industry and Energy
- Ministry of Health and Welfare
- Ministry of Food and Drug Safety [KMDF_PR_20200901_0095]
- National Research Foundation of Korea [2020R1A2C1014829] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
One of the challenging issues in classification problems is obtaining enough labeled data for training. Most datasets exhibit class imbalance, which biases the model towards the majority class. To address this issue, semi-supervised learning methods using additional unlabeled data have been proposed. In this study, we propose iterative semi-supervised learning algorithms that correct the labeling of extra unlabeled data based on softmax probabilities. The results show that our algorithms achieve high accuracy comparable to supervised learning. Tested on both balanced and imbalanced unlabeled datasets, our algorithms outperform previous state-of-the-art methods.
For the classification problem in practice, one of the challenging issues is to obtain enough labeled data for training. Moreover, even if such labeled data has been sufficiently accumulated, most datasets often exhibit long-tailed distribution with heavy class imbalance, which results in a biased model towards a majority class. To alleviate such class imbalance, semi-supervised learning methods using additional unlabeled data have been considered. However, as a matter of course, the accuracy is much lower than that from supervised learning. In this study, under the assumption that additional unlabeled data is available, we propose the iterative semi-supervised learning algorithms, which iteratively correct the labeling of the extra unlabeled data based on softmax probabilities. The results show that the proposed algorithms provide the accuracy as high as that from the supervised learning. To validate the proposed algorithms, we tested on the two scenarios: with the balanced unlabeled dataset and with the imbalanced unlabeled dataset. Under both scenarios, our proposed semi-supervised learning algorithms provided higher accuracy than previous state-of-the-arts. Code is available at https://github. com/HeewonChung92/iterative- semi-learning.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据