Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification Article Swipe
Yudi Zhao
,
Kuangrong Hao
,
Chaochen Gu
,
Bing Wei
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.12744
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2207.12744
To address the trade-off problem of quality-diversity for the generated images in imbalanced classification tasks, we research on over-sampling based methods at the feature level instead of the data level and focus on searching the latent feature space for optimal distributions. On this basis, we propose an iMproved Estimation Distribution Algorithm based Latent featUre Distribution Evolution (MEDA_LUDE) algorithm, where a joint learning procedure is programmed to make the latent features both optimized and evolved by the deep neural networks and the evolutionary algorithm, respectively. We explore the effect of the Large-margin Gaussian Mixture (L-GM) loss function on distribution learning and design a specialized fitness function based on the similarities among samples to increase diversity. Extensive experiments on benchmark based imbalanced datasets validate the effectiveness of our proposed algorithm, which can generate images with both quality and diversity. Furthermore, the MEDA_LUDE algorithm is also applied to the industrial field and successfully alleviates the imbalanced issue in fabric defect classification.
Related Topics
Concepts
Benchmark (surveying)
Artificial intelligence
Computer science
Evolutionary algorithm
Feature (linguistics)
Margin (machine learning)
Estimation of distribution algorithm
Machine learning
Pattern recognition (psychology)
Fitness function
Artificial neural network
Feature vector
Gaussian
Genetic algorithm
Geography
Quantum mechanics
Linguistics
Physics
Philosophy
Geodesy
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2207.12744
- https://arxiv.org/pdf/2207.12744
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4288099466
All OpenAlex metadata
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https://openalex.org/W4288099466Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2207.12744Digital Object Identifier
- Title
-
Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image ClassificationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-07-26Full publication date if available
- Authors
-
Yudi Zhao, Kuangrong Hao, Chaochen Gu, Bing WeiList of authors in order
- Landing page
-
https://arxiv.org/abs/2207.12744Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2207.12744Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2207.12744Direct OA link when available
- Concepts
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Benchmark (surveying), Artificial intelligence, Computer science, Evolutionary algorithm, Feature (linguistics), Margin (machine learning), Estimation of distribution algorithm, Machine learning, Pattern recognition (psychology), Fitness function, Artificial neural network, Feature vector, Gaussian, Genetic algorithm, Geography, Quantum mechanics, Linguistics, Physics, Philosophy, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.learning | 61, 98 |
| abstract_inverted_index.networks | 78 |
| abstract_inverted_index.proposed | 126 |
| abstract_inverted_index.research | 16 |
| abstract_inverted_index.validate | 121 |
| abstract_inverted_index.Algorithm | 50 |
| abstract_inverted_index.Evolution | 55 |
| abstract_inverted_index.Extensive | 114 |
| abstract_inverted_index.MEDA_LUDE | 139 |
| abstract_inverted_index.algorithm | 140 |
| abstract_inverted_index.benchmark | 117 |
| abstract_inverted_index.generated | 9 |
| abstract_inverted_index.optimized | 71 |
| abstract_inverted_index.procedure | 62 |
| abstract_inverted_index.searching | 33 |
| abstract_inverted_index.trade-off | 3 |
| abstract_inverted_index.Estimation | 48 |
| abstract_inverted_index.algorithm, | 57, 82, 127 |
| abstract_inverted_index.alleviates | 150 |
| abstract_inverted_index.diversity. | 113, 136 |
| abstract_inverted_index.imbalanced | 12, 119, 152 |
| abstract_inverted_index.industrial | 146 |
| abstract_inverted_index.programmed | 64 |
| abstract_inverted_index.(MEDA_LUDE) | 56 |
| abstract_inverted_index.experiments | 115 |
| abstract_inverted_index.specialized | 102 |
| abstract_inverted_index.Distribution | 49, 54 |
| abstract_inverted_index.Furthermore, | 137 |
| abstract_inverted_index.Large-margin | 90 |
| abstract_inverted_index.distribution | 97 |
| abstract_inverted_index.evolutionary | 81 |
| abstract_inverted_index.similarities | 108 |
| abstract_inverted_index.successfully | 149 |
| abstract_inverted_index.effectiveness | 123 |
| abstract_inverted_index.over-sampling | 18 |
| abstract_inverted_index.respectively. | 83 |
| abstract_inverted_index.classification | 13 |
| abstract_inverted_index.distributions. | 40 |
| abstract_inverted_index.classification. | 157 |
| abstract_inverted_index.quality-diversity | 6 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |