Online Bagging for Anytime Transfer Learning

Guokun Chi, Min Jiang, Xing Gao, Weizhen Hu, Shihui Guo, Kay Chen Tan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages941-947
Number of pages7
ISBN (Electronic)9781728124858
DOIs
Publication statusPublished - Dec 2019
Externally publishedYes
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 6 Dec 20199 Dec 2019

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
Country/TerritoryChina
CityXiamen
Period6/12/199/12/19

Keywords

  • ensemble learning
  • negative transfer
  • online bagging
  • online transfer learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Modelling and Simulation

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