On the Optimality of Classifier Chain for Multi-label Classification
Authors: Weiwei Liu, Ivor Tsang
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | 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 liuweiwei863@gmail.com, ivor.tsang@uts.edu.au |
| 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. |