多源域分布下优化权重的无监督迁移学习Boosting方法

深度决策树迁移学习Boosting方法（DTrBoost）可以有效地实现单源域有监督情况下向一个目标域迁移学习,但无法实现多个源域情况下的无监督迁移场景。针对这一问题,提出了多源域分布下优化权重的无监督迁移学习Boosting方法,主要思想是根据不同源域与目标域分布情况计算出对应的KL值,通过比较选择合适数量的不同源域样本训练分类器并对目标域样本打上伪标签。最后,依照各个不同源域的KL距离分配不同的学习权重,将带标签的各个源域样本与带伪标签的目标域进行集成训练得到最终结果。对比实验表明,提出的算法实现了更好的分类精度并对不同的数据集实现了自适应效果,分类错误率平均下降2.4%,在效果最好的marketing数据集上下降6%以上。

Unsupervised transfer learning Boosting for weight optimization under multi-source domain distribution

The deep decision tree migration learning boosting method(DtrBoost) can effectively realize the migration learning from a single source domain to a target domain under supervision, but can not realize the unsupervised migration scenario under multiple source domains. To solve this problem, this paper proposed an unsupervised transfer learning boosting method for optimizing the weight under multi-source domain distribution. The main idea was to calculate the corresponding KL value according to the distribution of different source domains and target domains, selected an appropriate number of samples from different source domains to train the classifier and pseudo label the samples from the target domain. Finally, the algorithm assigned different learning weights according to the KL distance of each different source domain, and the labeled source domain samples integrated to the pseudo labeled target domain to obtain the final result. Comparative experiments show that the proposed algorithm achieves better classification accuracy and adaptive effect on different data sets. The average classification error rate decreases by 2.4% and more than 6% on the best marketing data set.