Using A* for Inference in Probabilistic Classifier Chains
Authors: Deiner Mena, Elena Montañés, José Ramón Quevedo, Juan José del Coz
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |