Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty
Authors: Vu-Linh Nguyen, Sébastien Destercke, Marie-Hélène Masson, Eyke Hüllermeier
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section presents first experimental results to assess the performance of our approach to reliable classification. 6.1 Data Sets and Experimental Setting We perform experiments on 15 data sets from the UCI repository (cf. Table 1), following a 10 × 10-fold cross-validation procedure. We compare the performance of our method (referred to as PREORDER) with two competitors. |
| Researcher Affiliation | Academia | 1 UMR CNRS 7253 Heudiasyc, Sorbonne universit es, Universit e de technologie de Compi egne CS 60319 60203 Compi egne cedex, France 2 Universit e de Picardie Jules Verne, France 3 Department of Computer Science, Paderborn University, 33098 Paderborn, Germany |
| Pseudocode | No | The paper presents mathematical formulations and describes procedures textually but does not include any explicitly labeled "Pseudocode" or "Algorithm" blocks. |
| Open Source Code | No | The paper provides a link to a third-party library's documentation ("For an implementation in Python, see https://docs. scipy.org/doc/scipy/reference/generated/ scipy.optimize.minimize.html") but does not provide concrete access (e.g., a specific repository link or an explicit statement of code release) to the authors' own implementation of their described methodology. |
| Open Datasets | Yes | We perform experiments on 15 data sets from the UCI repository (cf. Table 1) |
| Dataset Splits | Yes | following a 10 × 10-fold cross-validation procedure. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper mentions 'Python' and refers to a 'scipy' function, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | where the regularization term γ will be fixed to 1. ... In practice, we evaluate (21) and (22) on uniform discretizations of cardinality 50 of [0.5, 1) and (0, 0.5], respectively. |