Affiliations: [a] School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan, China | [b] Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu, Sichuan, China | [c] Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing, China
Correspondence:
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Corresponding author: Fan Min, School of Computer Science, Southwest Petroleum University, Chengdu, Sichuan 610500, China. E-mail: [email protected].
Abstract: Co-training is a popular semi-supervised learning method. The learners exchange pseudo-labels obtained from different views to reduce the accumulation of errors. One of the key issues is how to ensure the quality of pseudo-labels. However, the pseudo-labels obtained during the co-training process may be inaccurate. In this paper, we propose a safe co-training (SaCo) algorithm for regression with two new characteristics. First, the safe labeling technique obtains pseudo-labels that are certified by both views to ensure their reliability. It differs from popular techniques of using two views to assign pseudo-labels to each other. Second, the label dynamic adjustment strategy updates the previous pseudo-labels to keep them up-to-date. These pseudo-labels are predicted using the augmented training data. Experiments are conducted on twelve datasets commonly used for regression testing. Results show that SaCo is superior to other co-training style regression algorithms and state-of-the-art semi-supervised regression algorithms.
