Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Reranking
Authors: Yan Yan, Junyuan Liu, Bo-Wen Zhang
JAIR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations on popular datasets including MAG-CS, Pub Med, and AAPD demonstrate the effectiveness and robustness of Label Co Rank. |
| Researcher Affiliation | Academia | YAN YAN, China University of Mining & Technology, Beijing, China JUNYUAN LIU, China University of Mining & Technology, Beijing, China BO-WEN ZHANG , University of Science and Technology Beijing, China |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations, and provides a block diagram in Fig. 1. However, it does not include explicitly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | Yes | The implementation code is publicly available on https://github.com/821code/Label Co Rank. |
| Open Datasets | Yes | The proposed method is evaluated on three well-known public datasets: MAG-CS, Pub Med, and AAPD. ... MAG-CS[32]: The dataset consists of 705,407 papers from the Microsoft Academic Graph (MAG), ... Pub Med[21]: The dataset comprises 898,546 papers sourced from Pub Med, ... AAPD[41]: The dataset comprises English abstracts of computer science papers sourced from arxiv.org, ... |
| Dataset Splits | Yes | Table 1. Dataset statistics.πtrn and πtst represent the number of documents in the training set and test set, respectively. ... MAG-CS 564340 70534 ... Pub Med 718837 89855 ... AAPD 54840 1000 |
| Hardware Specification | Yes | Experiments were conducted on a computer equipped with an Nvidia RTX 4090 GPU and 128 GB of RAM. |
| Software Dependencies | No | The paper mentions "The Adam W optimizer was employed" and "utilized the Ro BERTa pre-trained model as a feature extractor," but it does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used to implement the methodology. |
| Experiment Setup | Yes | The Adam W optimizer was employed with a learning rate of 1e-5, sentence truncation set to 512, and batch size of 16. The threshold hyperparameter, πΌ, was set to 0.3, and the hyperparameters for the loss function weight, π½, was set to 0.3, 0.3, and 0.25 for the MAG-CS, Pub Med, and AAPD datasets, respectively. The number of selected labels for these datasets πΎwas 30, 35, and 20, respectively. |