Multi-Positive and Unlabeled Learning
Authors: Yixing Xu, Chang Xu, Chao Xu, Dacheng Tao
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, our experimental results demonstrate the significance and effectiveness of the proposed algorithm in synthetic and real-world datasets. Experiments were conducted on four different datasets |
| Researcher Affiliation | Academia | Key Laboratory of Machine Perception (MOE), Cooperative Medianet Innovation Center, School of Electronics Engineering and Computer Science, PKU, Beijing 100871, China UBTech Sydney AI Institute, The School of Information Technologies, The University of Sydney, J12, 1 Cleveland St, Darlington, NSW 2008, Australia |
| Pseudocode | Yes | Algorithm 1 Multi-positive and unlabeled learning |
| Open Source Code | No | The paper does not provide any links or explicit statements about the availability of open-source code for the described methodology. |
| Open Datasets | Yes | We first conducted an experiment on the IRIS dataset from the UCI repository. Experiments were conducted on four different datasets, and the relevant metadata for each dataset are shown in Table 1. The datasets used are Image Segment, Letter, USPS, MNIST. |
| Dataset Splits | No | The paper mentions '20% test data' in Table 2, and describes how data was split into labeled and unlabeled portions: 'half of the samples in each positive class regarded as labeled, while the other half together with all the samples in the negative class regarded as unlabeled'. However, it does not explicitly state a dedicated 'validation' split or its size. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using 'Linear SVM' and implicitly other methods, but it does not specify any software names with version numbers for reproducibility (e.g., Python, specific libraries, or solvers with versions). |
| Experiment Setup | Yes | All datasets were preprocessed, with half of the samples in each positive class regarded as labeled, while the other half together with all the samples in the negative class regarded as unlabeled. All methods use linear kernal. the length of the codewords is fixed as r = k 1. The class priors were assumed to be known at the time of training. |