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|a 10.1109/TPAMI.2022.3178914
|2 doi
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|a eng
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| 100 |
1 |
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|a Wang, Xinyue
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Deep Generative Mixture Model for Robust Imbalance Classification
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|c 2023
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 07.04.2023
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|a Date Revised 11.04.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Discovering hidden pattern from imbalanced data is a critical issue in various real-world applications. Existing classification methods usually suffer from the limitation of data especially for minority classes, and result in unstable prediction and low performance. In this paper, a deep generative classifier is proposed to mitigate this issue via both model perturbation and data perturbation. Specially, the proposed generative classifier is derived from a deep latent variable model where two variables are involved. One variable is to capture the essential information of the original data, denoted as latent codes, which are represented by a probability distribution rather than a single fixed value. The learnt distribution aims to enforce the uncertainty of model and implement model perturbation, thus, lead to stable predictions. The other variable is a prior to latent codes so that the codes are restricted to lie on components in Gaussian Mixture Model. As a confounder affecting generative processes of data (feature/label), the latent variables are supposed to capture the discriminative latent distribution and implement data perturbation. Extensive experiments have been conducted on widely-used real imbalanced image datasets. Experimental results demonstrate the superiority of our proposed model by comparing with popular imbalanced classification baselines on imbalance classification task
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|a Journal Article
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1 |
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|a Jing, Liping
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Lyu, Yilin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Guo, Mingzhe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Jiaqi
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Liu, Huafeng
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yu, Jian
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zeng, Tieyong
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 3 vom: 03. März, Seite 2897-2912
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
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|g volume:45
|g year:2023
|g number:3
|g day:03
|g month:03
|g pages:2897-2912
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2022.3178914
|3 Volltext
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