University of Oulu

J. Wu, W. Zhuge, X. Liu, L. Liu and C. Hou, "Fragmentary Multi-Instance Classification," in IEEE Transactions on Cybernetics, vol. 51, no. 10, pp. 5156-5169, Oct. 2021, doi: 10.1109/TCYB.2019.2938206

Fragmentary multi-instance classification

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Author: Wu, Jie1; Zhuge, Wenzhang1; Liu, Xinwang2;
Organizations: 1Department of Systems Science, National University of Defense Technology, Changsha 410073, China
2School of Computer Science, National University of Defense Technology, Changsha 410073, China
3Center for Machine Vision and Signal Analysis, University of Oulu, 90014 Oulu, Finland
4College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2021
Publish Date: 2019-12-10


Multi-instance learning (MIL) has been extensively applied to various real tasks involving objects with bags of instances, such as in drugs and images. Previous studies on MIL assume that data are entirely complete. However, in many real tasks, the instance is fragmentary. In this article, we present probably the first study on multi-instance classification with fragmentary data. In our proposed framework, called fragmentary multi-instance classification (FIC), the fragmentary data are completed and the multi-instance classifier is learned jointly. To facilitate the integration between the completion and classifier learning, FIC establishes the weighting mechanism to measure the importance levels of different instances. To validate the compatibility of our framework, four typical MIL methods, including multi-instance support vector machine (MI-SVM), expectation maximization diverse density (EM-DD), citation-K nearest neighbors (Citation-KNNs), and MIL with discriminative bag mapping (MILDM), are embedded into the framework to obtain the corresponding FIC versions. As an illustration, an efficient solving algorithm is developed to address the problem for MI-SVM, together with the proof of convergence behavior. The experimental results on various types of real-world datasets demonstrate the effectiveness.

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Series: IEEE transactions on cybernetics
ISSN: 2168-2267
ISSN-E: 2168-2275
ISSN-L: 2168-2267
Volume: 51
Issue: 10
Pages: 5156 - 5169
DOI: 10.1109/TCYB.2019.2938206
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This work was supported in part by the NSF of China under Grant 61922087 and Grant 61906201, and in part by the NSF for Distinguished Young Scholars of Hunan Province under Grant 2019JJ20020.
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