中文摘要：

为了克服偏标记学习中监督信息缺失的问题，根据偏标记样本的性质设计决策树生成过程中的样本分裂规则，改造决策树的建立算法．文中算法首先对样本进行bootstrap采样并建立多棵决策树，然后对各决策树结果进行投票得出最终预测结果．在人工数据集和真实数据集上的实验表明，文中算法具有较好的分类性能．

英文摘要：

To overcome the problem of the missing supervision information in partial label learning, a special splitting measure for the generation of decision tree is designed according to the property of partial label examples and the growth algorithm of decision tree is modified. In the proposed algorithm, bootstrap sampling is employed to construct multiple decision trees, and then the final prediction result is obtained by voting on the classification results of each decision tree. Experiments on artificial datasets and real-world datasets validate the good performance of the proposed algorithm.