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..
Determining Expert Research Areas with Multi-Instance Learning of Hierarchical Multi-Label Classification Model
Authors: Tao Wu, Qifan Wang, Zhiwei Zhang, Luo Si
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments have been conducted to demonstrate the superior performance of proposed research with a real world application. |
| Researcher Affiliation | Academia | Tao Wu, Qifan Wang, Zhiwei Zhang, and Luo Si Computer Science Department, Purdue University West Lafayette, IN 47907, US EMAIL |
| Pseudocode | Yes | Algorithm 1 EM-HM3 for Multi-instance Hierarchical Multi-label Classification |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described, nor does it include an explicit statement about code release or a link to a repository. |
| Open Datasets | Yes | We test our algorithm with a public expertise database INDURE1. 1www.indure.org |
| Dataset Splits | Yes | Ten-fold cross validations are performed, where the regularization parameter is tuned by maximizing the sum of F1 values of all levels. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions the use of 'LIBSVM' but does not provide specific version numbers for it or any other software dependencies, which are required for reproducibility. |
| Experiment Setup | Yes | Linear kernel is used in all four methods to conduct fair comparisons. For MIMLSVM, the ratio γ is set to be 20%. For EM-HM3 and HM3, we use the following loss function: ℓ(y, v) = P j cj[yj = vj] where croot = 1, cj = cpa(j)/|sibl(j)| |