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 | Conference PDF | Archive PDF | Plain Text | 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 {wu577, wang868, zhan1187, lsi}@purdue.edu |
| 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)| |