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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Partial Multi-Label Learning via Multi-Subspace Representation
Authors: Ziwei Li, Gengyu Lyu, Songhe Feng
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments We conduct experiments on seven PML datasets, which are synthesized from widely-used MLL datasets including Emotions [Trohidis et al., 2008], Genbase [Diplaris et al., 2005], Medical [Pestian et al., 2007], Corel5k [Duygulu et al., 2002], Bibtex [Katakis et al., 2008], Eurlex-dc and Eurlexsm [Menc ıa and F urnkranz, 2008]. These datasets are added with redundant noise labels by the controlling parameter r. Here, r {1, 2, 3} represents the average number of false positive labels for training examples. Table 1 shows the characteristics of the experimental datasets. |
| Researcher Affiliation | Academia | Beijing Key Laboratory of Traffic Data Analysis and Mining School of Computer and Information Technology, Beijing Jiaotong University EMAIL |
| Pseudocode | No | The paper describes the optimization steps in Section 3.2 with mathematical equations but does not present them in a structured pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We conduct experiments on seven PML datasets, which are synthesized from widely-used MLL datasets including Emotions [Trohidis et al., 2008], Genbase [Diplaris et al., 2005], Medical [Pestian et al., 2007], Corel5k [Duygulu et al., 2002], Bibtex [Katakis et al., 2008], Eurlex-dc and Eurlexsm [Menc ıa and F urnkranz, 2008]. |
| Dataset Splits | Yes | Meanwhile, we use 10-fold cross-validation to train the model. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Parameters in MUSER method including α, β, γ are chosen from {10 3, 10 2, ..., 102, 103} with a grid search manner. Five widely-used multi-label metrics are employed to evaluate each comparing method... m and c are set to 50% of original feature and label space. |