Online Bagging for Anytime Transfer Learning
This work addresses the need for anytime transfer learning in practical applications where data is streamed, offering an incremental improvement over offline methods.
The paper tackles the problem of online transfer learning where data arrives sequentially, proposing a novel framework that applies online bagging to construct strong classifiers through iterative updates of weak classifiers, with experiments on three real datasets demonstrating its effectiveness.
Transfer learning techniques have been widely used in the reality that it is difficult to obtain sufficient labeled data in the target domain, but a large amount of auxiliary data can be obtained in the relevant source domain. But most of the existing methods are based on offline data. In practical applications, it is often necessary to face online learning problems in which the data samples are achieved sequentially. In this paper, We are committed to applying the ensemble approach to solving the problem of online transfer learning so that it can be used in anytime setting. More specifically, we propose a novel online transfer learning framework, which applies the idea of online bagging methods to anytime transfer learning problems, and constructs strong classifiers through online iterations of the usefulness of multiple weak classifiers. Further, our algorithm also provides two extension schemes to reduce the impact of negative transfer. Experiments on three real data sets show that the effectiveness of our proposed algorithms.