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..
Multi-Label Classification with Feature-Aware Non-Linear Label Space Transformation
Authors: Xin Li, Yuhong Guo
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on a number of multi-label classification datasets. The proposed approach demonstrates good performance, comparing to a number of stateof-the-art label dimension reduction methods. |
| Researcher Affiliation | Academia | Xin Li and Yuhong Guo Department of Computer and Information Sciences Temple University, Philadelphia, PA 19122, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 A Unified Training Algorithm |
| Open Source Code | No | No statement providing access to open-source code was found. |
| Open Datasets | Yes | We used five real world multi-label datasets for image and text categorization tasks in our experiments, including Corel5K, ESPGame, Iaprtc12, Enron, and Delicious. |
| Dataset Splits | Yes | We conducted experiments using 10-fold cross validation on four datasets, except the large scale dataset Delicious, on which we conducted experiments using 5-fold cross validation. In each cross validation iteration, we performed parameter selection for all the comparison methods by using 80% of the training set for training and the remaining 20% for performance evaluation. |
| Hardware Specification | Yes | To compare the empirical computational complexity of the comparison methods, we reported in Table 2 the training time and testing time of each method for a single run with θ=0.3 on a 64-bit PC with 4 processors (3.4 GHz) and 16 GB memory. |
| Software Dependencies | No | No specific software dependencies with version numbers were mentioned. |
| Experiment Setup | Yes | For the proposed COMB method, there are two parameters µ and γ to be tuned for the decoding process. We selected the µ value from the set [0.001, 0.005, 0.01, 0.05, 0.1], and selected the γ value from the set [0, 0.2, 0.4, 0.6, 0.8, 1]. |