Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Using A* for Inference in Probabilistic Classifier Chains
Authors: Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,969 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,978 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments reported here were performed over several benchmark datasets previously used in many MLC papers. |
| Researcher Affiliation | Academia | a Artificial Intelligence Center, University of Oviedo at Gij on, (Asturias) Spain b Universidad Tecnol ogica del Choc o, Colombia |
| Pseudocode | No | The paper describes the algorithms in text and with mathematical formulas, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., a specific repository link or an explicit statement of code release) for the source code of its methodology. |
| Open Datasets | Yes | The experiments reported here were performed over several benchmark datasets previously used in many MLC papers. The main characteristic for these experiments is the number of labels, which varies between 5 and 101. Datasets: emotions, enron, flags, mediamill, medical, reuters, slashdot, yeast. |
| Dataset Splits | Yes | The results will be presented in terms of the subset 0/1 loss estimated by means of a 10-fold cross-validation. The regularization parameter C was established for each individual binary classifier performing a grid search over the values C {10 3, 10 2, . . . , 102, 103} optimizing the brier loss [Brier, 1950] estimated by means of a balanced 2-fold cross validation repeated 5 times. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'logistic regression [Lin et al., 2008]' as the base learner but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The regularization parameter C was established for each individual binary classifier performing a grid search over the values C {10 3, 10 2, . . . , 102, 103} optimizing the brier loss [Brier, 1950] estimated by means of a balanced 2-fold cross validation repeated 5 times. |