一种新的基于半监督的多标记学习算法

A new multi-label learning algorithm based on semi-supervised learning

多标记学习中通常存在大量未标记示例,本研究结合协同训练(Co-training)方法充分利用数据集中的未标记示例,在数据集上选取局部k-NN(k nearest neighbor)和全局k-NN进行训练得到两个分类器,分类器分别标记未标记示例并相互更新训练集。协同训练过程不断迭代进行,直至训练完成。试验结果表明,该方法性能均优于其他多标记学习算法。

Multi-label learning usually has many unlabeled samples.Combined with co-training method,this research made full use of the unlabeled sampled in dataset,selected the local k-NN（k nearest neighbor） and global k-NN for training to get two classifiers,which could label the unlabeled examples and could be added to the training set.The collaborative training process iterated continuously,until the training finished.The experimental results showed that this algorithm could outperform other multi-label learning algorithms.