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Double-bootstrapping source data selection for instance-based transfer learning

Authors:

Jian ZhangAuthors Info & Claims

Pattern Recognition Letters, Volume 34, Issue 11

Pages 1279 - 1285

https://doi.org/10.1016/j.patrec.2013.04.012

Published: 01 August 2013 Publication History

Abstract

Instance-based transfer is an important paradigm for transfer learning, where data from related tasks (source data) are combined with the data for the current learning task (target data) to train a learner for the current (target) task. However, in most application scenarios, the benefit of the source data is unclear. The source may contain both helpful and harmful instances to the target learning. Simply combining the source with the target data may result in performance deterioration (negative transfer). Selecting the instances from the source data that will benefit the target task is a key step for instance-based transfer learning. Most existing instance-based transfer methods lack such selection or mix source selection with the training for the target task. This leads to problems as the training may use source data harmful to the target. We propose a simple yet effective method for instance-based transfer learning in environments where the usefulness of the sources are unclear. The method employs a double-selection process, based on bootstrapping, to reduce the impact of irrelevant/harmful data in the source. Experiment results show that in most cases, our method produces more improvements through transfer than TrBagg (Kamishima et al., 2009) and TrAdaBoost (Dai et al., 2009). Our method can also deal with a wider range of transfer learning scenarios.

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Wu CChen JFang QHe KZhao ZRen HXu GLiu YXiang Y(2024)Rethinking Membership Inference Attacks Against Transfer LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341359219(6441-6454)Online publication date: 1-Jan-2024
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Luo WFeng PZhang JYu DWu Z(2022)A novel deep convolution generative adversarial transfer learning model for data-driven assembly quality diagnosisJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21186042:3(2727-2741)Online publication date: 1-Jan-2022
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Double-bootstrapping source data selection for instance-based transfer learning
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image Pattern Recognition Letters

Pattern Recognition Letters Volume 34, Issue 11

August, 2013

109 pages

ISSN:0167-8655

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Copyright © Elsevier B.V. © 2013.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 August 2013

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Cited By

Wu CChen JFang QHe KZhao ZRen HXu GLiu YXiang Y(2024)Rethinking Membership Inference Attacks Against Transfer LearningIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.341359219(6441-6454)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TIFS.2024.3413592
Luo WFeng PZhang JYu DWu Z(2022)A novel deep convolution generative adversarial transfer learning model for data-driven assembly quality diagnosisJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-21186042:3(2727-2741)Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.3233/JIFS-211860
Chen SXu ZWang XZhang C(2022)Ambient air pollutants concentration prediction during the COVID-19Knowledge-Based Systems10.1016/j.knosys.2022.109996258:COnline publication date: 22-Dec-2022
https://dl.acm.org/doi/10.1016/j.knosys.2022.109996
Deng CTang JMiao JZhao YChen XLu S(2022)Efficient stability prediction of milling process with arbitrary tool-holder combinations based on transfer learningJournal of Intelligent Manufacturing10.1007/s10845-022-01912-534:5(2263-2279)Online publication date: 14-Feb-2022
https://dl.acm.org/doi/10.1007/s10845-022-01912-5
Qin XChen YWang JYu C(2020)Cross-Dataset Activity Recognition via Adaptive Spatial-Temporal Transfer LearningProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/33698183:4(1-25)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.1145/3369818
Zhang YXu JMiao GChen YZhang Y(2018)Addressing Domain Adaptation for Chinese Word Segmentation with Instances-Based Transfer LearningChinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data10.1007/978-3-030-01716-3_3(24-36)Online publication date: 19-Oct-2018
https://dl.acm.org/doi/10.1007/978-3-030-01716-3_3
Zhou SSmirnov ESchoenmakers GDriessens KPeeters R(2017)Testing exchangeability for transfer decisionPattern Recognition Letters10.1016/j.patrec.2016.12.02188:C(64-71)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.patrec.2016.12.021
Zhou SSmirnov ESchoenmakers GPeeters R(2017)Conformal decision-tree approach to instance transferAnnals of Mathematics and Artificial Intelligence10.1007/s10472-017-9554-x81:1-2(85-104)Online publication date: 1-Oct-2017
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Inoue SPan XHara TShigeno H(2016)Supervised and Unsupervised Transfer Learning for Activity Recognition from Simple In-home SensorsProceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services10.1145/2994374.2994400(20-27)Online publication date: 28-Nov-2016
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Zhao SCao QChen JZhang YTang JDuan Z(2016)A multi-ATL method for transfer learning across multiple domains with arbitrarily different distributionKnowledge-Based Systems10.1016/j.knosys.2015.10.03294:C(60-69)Online publication date: 15-Feb-2016
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