research-article

Multiple instance learning

Authors:

Marc-Andr Carbonneau,

Veronika Cheplygina,

Ghyslain GagnonAuthors Info & Claims

Pattern Recognition, Volume 77, Issue C

Pages 329 - 353

https://doi.org/10.1016/j.patcog.2017.10.009

Published: 01 May 2018 Publication History

Abstract

The characteristics specific of MIL problems are formally identified and described.MIL methods and applications are reviewed in the light of the problem characteristics.Comparative experiments show the impact of problem characteristics on 16 reference methods.Recommendation are issued for future benchmarking.Promising avenue of research are identified. Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research. Code is available on-line at https://github.com/macarbonneau/MILSurvey.

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Yin SZhao SWang HXu TChen E(2024)Exploiting Instance-level Relationships in Weakly Supervised Text-to-Video RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366357120:10(1-21)Online publication date: 12-Sep-2024
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cover image Pattern Recognition

Pattern Recognition Volume 77, Issue C

May 2018

415 pages

ISSN:0031-3203

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Published: 01 May 2018

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Yin SZhao SWang HXu TChen E(2024)Exploiting Instance-level Relationships in Weakly Supervised Text-to-Video RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366357120:10(1-21)Online publication date: 12-Sep-2024
https://dl.acm.org/doi/10.1145/3663571
Shu SWang DYuan SWei HJiang JFeng LZhang M(2024)Multiple-instance Learning from Triplet Comparison BagsACM Transactions on Knowledge Discovery from Data10.1145/363877618:4(1-18)Online publication date: 12-Feb-2024
https://dl.acm.org/doi/10.1145/3638776
Xie ZLiu YHe HLi MZhou Z(2024)Weakly Supervised AUC Optimization: A Unified Partial AUC ApproachIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.335781446:7(4780-4795)Online publication date: 24-Jan-2024
https://dl.acm.org/doi/10.1109/TPAMI.2024.3357814
Chen JPu JYin BZhang RWu J(2024)TA-NET: Empowering Highly Efficient Traffic Anomaly Detection Through Multi-Head Local Self-Attention and Adaptive Hierarchical Feature ReconstructionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.336582025:9(12372-12384)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1109/TITS.2024.3365820
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Huang SLiu ZJin WMu Y(2024)Superpixel-based multi-scale multi-instance learning for hyperspectral image classificationPattern Recognition10.1016/j.patcog.2024.110257149:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.patcog.2024.110257
Morales-Álvarez PSchmidt AHernández-Lobato JMolina R(2024)Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological imagesPattern Recognition10.1016/j.patcog.2023.110057146:COnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.patcog.2023.110057
Yu QZhao NLi MLi ZWang HZhang WSui KPei D(2024)A survey on intelligent management of alerts and incidents in IT servicesJournal of Network and Computer Applications10.1016/j.jnca.2024.103842224:COnline publication date: 1-Apr-2024
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Yin JLiu XYang Z(2024)A deep multiple-instance text binary classification for topic relevant content extraction on social mediaJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2023.10188336:1Online publication date: 17-Apr-2024
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