一种基于局部分类精度的多源在线迁移学习算法

近年来，迁移学习得到越来越多的关注.现有的在线迁移学习算法一般从单个源领域迁移知识，然而，当源领域与目标领域相似度较低时，很难进行有效的迁移学习.基于此，提出了一种基于局部分类精度的多源在线迁移学习方法——LC-MSOTL.LC-MSOTL存储多个源领域分类器，计算新到样本与目标领域已有样本之间的距离以及各源领域分类器对其最近邻样本的分类精度，从源领域分类器中挑选局部精度最高的分类器与目标领域分类器加权组合，从而实现多个源领域知识到目标领域的迁移学习.在人工数据集和实际数据集上的实验结果表明，LC-MSOTL能够有效地从多个源领域实现选择性迁移，相对于单源在线迁移学习算法OTL，显示出了更高的分类准确率.

Online Transfer Learning from Multiple Sources Based on Local Classification Accuracy

In recent years, transfer learning has gained more and more attention. However, most of the existing online transfer learning methods transfer knowledge from a single source, and it is hard to make effective transfer learning when the similarity between source domain and target domain is low. To solve this problem, this paper proposes a multi-source online transfer learning method, LC-MSOTL, based on local classification accuracy. LC-MSOTL stores multiple classifiers each trained on a different source, computes the distance between the new arrived sample and its k-nearest neighbor samples in the target domain as well as the local classification accuracies of each source domain classifier on the k-nearest neighbor samples, selects the classifier with the highest local classification accuracy from source domain classifiers and combines it with the target domain classifier, so as to realize the knowledge transfer from multi-source domains to a target domain. Experiments on artificial datasets and real datasets illustrates that LC-MSOTL can effectively transfer knowledge selectively from multi-source domains, and can get higher classification accuracy compared with the single source online transfer learning algorithm OTL.