A New Image Oversampling Method Based on Influence Functions and Weights
Although imbalanced data have been studied for many years, the problem of data imbalance is still a major problem in the development of machine learning and artificial intelligence. The development of deep learning and artificial intelligence has further expanded the impact of imbalanced data, so st...
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MDPI AG
2024-11-01
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| author | Jun Ye Shoulei Lu Jiawei Chen |
| author_facet | Jun Ye Shoulei Lu Jiawei Chen |
| author_sort | Jun Ye |
| collection | DOAJ |
| description | Although imbalanced data have been studied for many years, the problem of data imbalance is still a major problem in the development of machine learning and artificial intelligence. The development of deep learning and artificial intelligence has further expanded the impact of imbalanced data, so studying imbalanced data classification is of practical significance. We propose an image oversampling algorithm based on the influence function and sample weights. Our scheme not only synthesizes high-quality minority class samples but also preserves the original features and information of minority class images. To address the lack of visually reasonable features in SMOTE when synthesizing images, we improve the pre-training model by removing the pooling layer and the fully connected layer in the model, extracting the important features of the image by convolving the image, executing SMOTE interpolation operation on the extracted important features to derive the synthesized image features, and inputting the features into a DCGAN network generator, which maps these features into the high-dimensional image space to generate a realistic image. To verify that our scheme can synthesize high-quality images and thus improve classification accuracy, we conduct experiments on the processed CIFAR10, CIFAR100, and ImageNet-LT datasets. |
| format | Article |
| id | doaj-art-0fce0a1fd8b646f59a8ed6eb9654c708 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-0fce0a1fd8b646f59a8ed6eb9654c7082025-08-20T02:08:07ZengMDPI AGApplied Sciences2076-34172024-11-0114221055310.3390/app142210553A New Image Oversampling Method Based on Influence Functions and WeightsJun Ye0Shoulei Lu1Jiawei Chen2Key Laboratory of Internet Information Retrieval of Hainan Province, School of Cyberspace Security, Hainan University, Haikou 570228, ChinaKey Laboratory of Internet Information Retrieval of Hainan Province, School of Cyberspace Security, Hainan University, Haikou 570228, ChinaSchool of Cyberspace Security, Hainan University, Haikou 570228, ChinaAlthough imbalanced data have been studied for many years, the problem of data imbalance is still a major problem in the development of machine learning and artificial intelligence. The development of deep learning and artificial intelligence has further expanded the impact of imbalanced data, so studying imbalanced data classification is of practical significance. We propose an image oversampling algorithm based on the influence function and sample weights. Our scheme not only synthesizes high-quality minority class samples but also preserves the original features and information of minority class images. To address the lack of visually reasonable features in SMOTE when synthesizing images, we improve the pre-training model by removing the pooling layer and the fully connected layer in the model, extracting the important features of the image by convolving the image, executing SMOTE interpolation operation on the extracted important features to derive the synthesized image features, and inputting the features into a DCGAN network generator, which maps these features into the high-dimensional image space to generate a realistic image. To verify that our scheme can synthesize high-quality images and thus improve classification accuracy, we conduct experiments on the processed CIFAR10, CIFAR100, and ImageNet-LT datasets.https://www.mdpi.com/2076-3417/14/22/10553data imbalanceimage oversamplingSMOTE interpolationDCGAN |
| spellingShingle | Jun Ye Shoulei Lu Jiawei Chen A New Image Oversampling Method Based on Influence Functions and Weights Applied Sciences data imbalance image oversampling SMOTE interpolation DCGAN |
| title | A New Image Oversampling Method Based on Influence Functions and Weights |
| title_full | A New Image Oversampling Method Based on Influence Functions and Weights |
| title_fullStr | A New Image Oversampling Method Based on Influence Functions and Weights |
| title_full_unstemmed | A New Image Oversampling Method Based on Influence Functions and Weights |
| title_short | A New Image Oversampling Method Based on Influence Functions and Weights |
| title_sort | new image oversampling method based on influence functions and weights |
| topic | data imbalance image oversampling SMOTE interpolation DCGAN |
| url | https://www.mdpi.com/2076-3417/14/22/10553 |
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