Online Bagging for Anytime Transfer Learning

TL;DR

This paper introduces an online bagging ensemble framework for transfer learning that operates in real-time, improving classifier performance and reducing negative transfer effects in sequential data scenarios.

Contribution

It presents a novel online transfer learning framework using bagging, enabling anytime learning and addressing negative transfer in sequential data environments.

Findings

Effective in real-world datasets

Reduces negative transfer impact

Operates in online, real-time settings

Abstract

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.