Online Bagging for Anytime Transfer Learning

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

2 Scopus Citations
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Author(s)

  • Guokun CHI
  • Min JIANG
  • Xing GAO
  • Weizhen HU
  • Shihui GUO

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence
PublisherIEEE
Pages941-947
ISBN (Electronic)978-1-7281-2485-8
Publication statusPublished - Dec 2019

Publication series

NameIEEE Symposium Series on Computational Intelligence, SSCI

Conference

Title2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PlaceChina
CityXiamen
Period6 - 9 December 2019

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.

Research Area(s)

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

Citation Format(s)

Online Bagging for Anytime Transfer Learning. / CHI, Guokun; JIANG, Min; GAO, Xing; HU, Weizhen; GUO, Shihui; TAN, Kay Chen.

2019 IEEE Symposium Series on Computational Intelligence. IEEE, 2019. p. 941-947 9002755 (IEEE Symposium Series on Computational Intelligence, SSCI).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review