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..
On the Optimality of Classifier Chain for Multi-label Classification
Authors: Weiwei Liu, Ivor Tsang
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments on a number of real-world multi-label data sets from various domains demonstrate that our proposed CC-DP algorithm outperforms state-of-the-art approaches and the CCGreedy algorithm achieves comparable prediction performance with CC-DP. |
| Researcher Affiliation | Academia | Weiwei Liu Ivor W. Tsang Centre for Quantum Computation and Intelligent Systems University of Technology, Sydney EMAIL, EMAIL |
| Pseudocode | No | While the algorithms are described textually (e.g., 'The CC-DP algorithm is shown as the following bottom-up procedure'), there are no formally structured pseudocode or algorithm blocks in the main text. The details for CC-Greedy are stated to be in the Supplementary Materials. |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing the source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We conduct experiments on eight real-world data sets with various domains from three websites.345 |
| Dataset Splits | Yes | We perform 5-fold cross-validation on each data set and report the mean and standard error of each evaluation measurement. |
| Hardware Specification | Yes | All the methods are implemented in Matlab and all experiments are conducted on a workstation with a 3.2GHZ Intel CPU and 4GB main memory running 64-bit Windows platform. |
| Software Dependencies | No | The paper mentions using 'LIBLINEAR' [21] but does not specify a version number for this or any other software dependency, which is required for reproducibility. |
| Experiment Setup | Yes | ECC is averaged over several CC predictions with random order and the ensemble size in ECC is set to 10 according to [5, 6]. ... We perform 5-fold cross-validation on each data set and report the mean and standard error of each evaluation measurement. |