Positive-Unlabeled Learning from Imbalanced Data
Authors: Guangxin Su, Weitong Chen, Miao Xu
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the performance of our proposed Imbalancednn PU with state-of-the-art PU methods on imbalanced datasets. We will show that state-of-the-art PU methods, although have been shown to be effective on balanced PU data, fails to be superior on imbalanced PU data. [...] Dataset. In previous PU work, datasets such as CIFAR10 have been widely used [...] Evaluation. The same as [Kiryo et al., 2017], we set the number of epochs to be 200. [...] Results without meta-learning. The experimental results of methods without meta-learning on F1 score are shown in Figure 2. |
| Researcher Affiliation | Academia | Guangxin Su1 , Weitong Chen1 , Miao Xu1,2 1The University of Queensland, Brisbane QLD4072, Australia 2RIKEN AIP, Tokyo 103-0027, Japan guangxinsu6@gmail.com, {weitong.chen, miao.xu}@uq.edu.au |
| Pseudocode | Yes | Algorithm 1 Imbalancednn PU Input: Training data P and U Parameter: class prior π and π , MAX_E Output: classifier bg(x; θ) |
| Open Source Code | No | The paper states 'All the codes are implemented in Python 3 and Pytorch 1.7' but does not provide a specific link or explicit statement about the public availability of their source code for the proposed method. |
| Open Datasets | Yes | In our tasks, following existing works to test the scalability of our proposal, we also use the CIFAR10 data. [...] 1https://www.cs.toronto.edu/ kriz/cifar.html |
| Dataset Splits | No | In each dataset, there are 50,000 training data and 10,000 test data as provided by the original CIFAR10. To make the training data into a PU learning problem, we follow [Kiryo et al., 2017] to sample 1,000 positive instances and treat them as P; all the training data are used as U, i.e., nu = 50,000. |
| Hardware Specification | No | All the codes are implemented in Python 3 and Pytorch 1.7, and running on a GPU server with CUDA 11.1. |
| Software Dependencies | Yes | All the codes are implemented in Python 3 and Pytorch 1.7, and running on a GPU server with CUDA 11.1. |
| Experiment Setup | Yes | For our proposed Imbalancednn PU, we set π = 0.5 and π = 0.1. We use the same network structure as [Kiryo et al., 2017], i.e., a 13-layer CNN with Re LU and Adam as the optimizer. We tune the hyper-parameters step size and weight decay by a grid select from {10-10, 10-9, . . . , 100} for all methods based on neural networks. All the other hyperparameters in the network are set as default. The same as [Kiryo et al., 2017], we set the number of epochs to be 200. |