Oversampling for Imbalanced Data via Optimal Transport
Authors: Yuguang Yan, Mingkui Tan, Yanwu Xu, Jiezhang Cao, Michael Ng, Huaqing Min, Qingyao Wu5605-5612
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on toy and real-world data sets demonstrate the efficacy of our proposed method in terms of multiple metrics. |
| Researcher Affiliation | Collaboration | Yuguang Yan,1,2 Mingkui Tan,1 Yanwu Xu,3 Jiezhang Cao,1 Michael Ng,4 Huaqing Min,1 Qingyao Wu1 1School of Software Engineering, South China University of Technology, China 2CVTE Research, Guangzhou Shiyuan Electronics Co., Ltd., China 3Artificial Intelligence Innovation Business, Baidu Inc., China 4Department of Mathematics, Hong Kong Baptist University, Hong Kong, China |
| Pseudocode | Yes | Algorithm 1 Optimal Transport for Over Sampling (OTOS) |
| Open Source Code | No | The paper does not provide a direct link to source code or explicitly state that the code is publicly released. |
| Open Datasets | Yes | We adopt six benchmark data sets from LIBSVM (australian, breast-cancer, diabetes, german, svmguide2, and svmguide4) in the experiments. For the data sets that are split into training and testing subsets, we only adopt training subsets for simplicity. For the multi-class data sets, we take one class as positive and the others as negative to construct imbalanced binary classification tasks. [...] We also use four fundus image data sets, among which i See-AMD, i See-DR, i See-glaucoma are used to detect Age-Related Macular Degeneration (AMD), Diabetic Retinopathy (DR) and glaucoma, respectively, and ORIGA is used to detect glaucoma. |
| Dataset Splits | Yes | results of each time are obtained by the mean of 10-fold cross-validation. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. It does mention extracting features using a ResNet-152, but not the hardware that ran the training/inference. |
| Software Dependencies | No | The paper states using 'linear SVM' and drawing random samples from 'uniform distribution U(0, 1)' but does not provide specific version numbers for any software, libraries, or frameworks used. |
| Experiment Setup | Yes | For all the compared methods, we synthesize minority class samples until that the numbers of minority and majority class samples are the same, and use linear SVM with the default parameter C = 1 as the classifier. For our method, we draw random samples from a prior uniform distribution U(0, 1). The parameters λ and ϵ are selected in the range 10{−1,0,1,2,3,4,5}, and the best results are adopted. We repeat all the experiments 10 times and report the mean and standard derivation values, and results of each time are obtained by the mean of 10-fold cross-validation. |