结合集成学习与迁移学习的标签比例学习方法

Label proportion learning method combining integrated learning and transfer learning

标签比例学习(LLP)是一种将实例放入包中的机器学习方法,它只提供包中的实例信息和标签比例信息,而不提供标签信息。针对多个相关任务的LLP问题,提出了一种基于迁移学习的标签比例集成学习模型,简称AT-LLP,该模型通过在任务之间构建共享参数来连接相关任务,将源任务中学习到的知识迁移到目标任务中,从而提高目标任务的学习效率。同时该算法引入了集成学习算法,在分类器多轮迭代的学习过程中,不断调整训练集的权重系数,进一步将弱分类器训练为强分类器。实验表明,所提AT-LLP模型比现有LLP方法具有更好的性能。

LLP is a machine learning method that puts instances into packages.It only provides instance information and label proportional information in packages,but does not provide label information.Aiming at the LLP problem of multiple related tasks,this paper proposed a label proportion integrated learning model based on transfer learning,called AT-LLP for short.The model connected related tasks by constructing shared parameters between tasks,and migrated the knowledge learned in the source task to the target task,so as to improve the learning efficiency of the target task.At the same time,the algorithm introduced the ensemble learning algorithm,which continuously adjusted the weight coefficient of the training set in the multi-iteration learning process of the classifier,so as to further train the weak classifier into a strong classifier.Experiments show that the proposed AT-LLP model has better performance than the existing LLP method.