research-article

Multi-instance learning by treating instances as non-I.I.D. samples

Authors:

Yu-Feng LiAuthors Info & Claims

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

Pages 1249 - 1256

https://doi.org/10.1145/1553374.1553534

Published: 14 June 2009 Publication History

Abstract

Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely independent in real tasks, and a better performance can be expected if the instances are treated in an non-i.i.d. way that exploits relations among instances. In this paper, we propose two simple yet effective methods. In the first method, we explicitly map every bag to an undirected graph and design a graph kernel for distinguishing the positive and negative bags. In the second method, we implicitly construct graphs by deriving affinity matrices and propose an efficient graph kernel considering the clique information. The effectiveness of the proposed methods are validated by experiments.

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Cited By

Xiao YLiu BHao Z(2025)Multi-Instance Nonparallel Tube LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.334744936:2(2563-2577)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2023.3347449
Yuan MXu YZhao J(2025)Multi-instance learning in the presence of positive and unlabeled bagsExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125551261:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125551
Lai QVong CYan TWong PLiang X(2025)Hybrid multiple instance learning network for weakly supervised medical image classification and localizationExpert Systems with Applications10.1016/j.eswa.2024.125362260(125362)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125362
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Index Terms

Multi-instance learning by treating instances as non-I.I.D. samples
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Model development and analysis
      1. Model verification and validation
      2. Modeling methodologies
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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Published In

cover image ACM Other conferences

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

June 2009

1331 pages

ISBN:9781605585161

DOI:10.1145/1553374

General Chair:
Andrea Danyluk
Williams College
,
Program Chairs:
Léon Bottou
NEC Laboratories America
,
Michael Littman
Rutgers University

Copyright © 2009 Copyright 2009 by the author(s)/owner(s).

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NSF
Microsoft Research: Microsoft Research
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2009

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ICML '09: The 26th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 14 - 18, 2009

Quebec, Montreal, Canada

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Xiao YLiu BHao Z(2025)Multi-Instance Nonparallel Tube LearningIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2023.334744936:2(2563-2577)Online publication date: Feb-2025
https://doi.org/10.1109/TNNLS.2023.3347449
Yuan MXu YZhao J(2025)Multi-instance learning in the presence of positive and unlabeled bagsExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125551261:COnline publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1016/j.eswa.2024.125551
Lai QVong CYan TWong PLiang X(2025)Hybrid multiple instance learning network for weakly supervised medical image classification and localizationExpert Systems with Applications10.1016/j.eswa.2024.125362260(125362)Online publication date: Jan-2025
https://doi.org/10.1016/j.eswa.2024.125362
Yang MZhang JPan ZMin F(2025)Multi-instance embedding space set-kernel fusion with discriminability metricApplied Intelligence10.1007/s10489-024-06160-z55:6Online publication date: 1-Apr-2025
https://dl.acm.org/doi/10.1007/s10489-024-06160-z
Tang WZhang WZhang MLarson K(2024)Exploiting conjugate label information for multi-instance partial-label learningProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/550(4973-4981)Online publication date: 3-Aug-2024
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Theodore Armand TBhattacharjee SKim H(2024)Overview of the Potentials of Multiple Instance Learning in Cancer Diagnosis: Applications, Challenges, and Future Directions2024 26th International Conference on Advanced Communications Technology (ICACT)10.23919/ICACT60172.2024.10471995(419-425)Online publication date: 4-Feb-2024
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Tao LHuang YDai WJiang YWooldridge MDy JNatarajan S(2024)Deciphering raw data in neuro-symbolic learning with provable guaranteesProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i14.29455(15310-15318)Online publication date: 20-Feb-2024
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Xiong DPark SLim JWang TWang X(2024)Bayesian multiple instance classification based on hierarchical probit regressionThe Annals of Applied Statistics10.1214/23-AOAS178018:1Online publication date: 1-Mar-2024
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Zhou SShu SWang HWei HXiang TLi B(2024)Multiple-Instance Learning from Pairwise Comparison BagsACM Transactions on Intelligent Systems and Technology10.1145/369646015:6(1-22)Online publication date: 29-Sep-2024
https://dl.acm.org/doi/10.1145/3696460
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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