Learning to sample: An active learning framework

Jingyu Shao, Qing Wang, Fangbing Liu

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    18 Citations (Scopus)

    Abstract

    Meta-learning algorithms for active learning are emerging as a promising paradigm for learning the 'best' active learning strategy. However, current learning-based active learning approaches still require sufficient training data so as to generalize meta-learning models for active learning. This is contrary to the nature of active learning which typically starts with a small number of labeled samples. The unavailability of large amounts of labeled samples for training meta-learning models would inevitably lead to poor performance (e.g., instabilities and overfitting). In our paper, we tackle these issues by proposing a novel learning-based active learning framework, called Learning To Sample (LTS). This framework has two key components: a sampling model and a boosting model, which can mutually learn from each other in iterations to improve the performance of each other. Within this framework, the sampling model incorporates uncertainty sampling and diversity sampling into a unified process for optimization, enabling us to actively select the most representative and informative samples based on an optimized integration of uncertainty and diversity. To evaluate the effectiveness of the LTS framework, we have conducted extensive experiments on three different classification tasks: image classification, salary level prediction, and entity resolution. The experimental results show that our LTS framework significantly outperforms all the baselines when the label budget is limited, especially for datasets with highly imbalanced classes. In addition to this, our LTS framework can effectively tackle the cold start problem occurring in many existing active learning approaches.

    Original languageEnglish
    Title of host publicationProceedings - 19th IEEE International Conference on Data Mining, ICDM 2019
    EditorsJianyong Wang, Kyuseok Shim, Xindong Wu
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages538-547
    Number of pages10
    ISBN (Electronic)9781728146034
    DOIs
    Publication statusPublished - Nov 2019
    Event19th IEEE International Conference on Data Mining, ICDM 2019 - Beijing, China
    Duration: 8 Nov 201911 Nov 2019

    Publication series

    NameProceedings - IEEE International Conference on Data Mining, ICDM
    Volume2019-November
    ISSN (Print)1550-4786

    Conference

    Conference19th IEEE International Conference on Data Mining, ICDM 2019
    Country/TerritoryChina
    CityBeijing
    Period8/11/1911/11/19

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