Graph-based boosting algorithm to learn labeled and unlabeled data

Highlights：

• Some ensemble learning algorithms were proposed to exploit the information of unlabeled data. These methods had to learn the samples with pseudo-labels due to the scarcity of labeled data. But it is inevitable for the samples with pseudo-labels to bring wrong information during training process. In this paper, we propose a novel graph-based boosting algorithm (GBB) to learn labeled and unlabeled data. And pseudo-labels will not occur during training process.

• GBB is a framework combining many models linearly and the similarity matrix of samples is transformed during training process.

• We also extend GBB, termed as weighted GBB (WGBB), to learn imbalanced data.

• Experimental results illustrate that GBB can achieve a competitive performance and WGBB has an obvious advantage to handle classification problem of imbalanced data, comparing with other related algorithms.