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..
Online F-Measure Optimization
Authors: Róbert Busa-Fekete, Balázs Szörényi, Krzysztof Dembczynski, Eyke Hüllermeier
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, first experimental results are presented, showing that our method performs well in practice. |
| Researcher Affiliation | Academia | R obert Busa-Fekete Department of Computer Science University of Paderborn, Germany; Bal azs Sz or enyi Technion, Haifa, Israel / MTA-SZTE Research Group on Artificial Intelligence, Hungary; Krzysztof Dembczy nski Institute of Computing Science Pozna n University of Technology, Poland; Eyke H ullermeier Department of Computer Science University of Paderborn, Germany |
| Pseudocode | Yes | Algorithm 1 OFO |
| Open Source Code | No | The paper does not contain an explicit statement or a direct link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We used in the experiments nine datasets taken from the Lib SVM repository of binary classification tasks.4 [Footnote 4: http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html] |
| Dataset Splits | Yes | We run OFO along with the three classifiers trained on 80% of the data. The rest 20% of the data was used to evaluate g t t in terms of the F-measure. ... As a baseline, we applied the 2S approach. More concretely, we trained the same set of learners on 60% of the data and validated the threshold on 20% by optimizing (6). |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact CPU or GPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like "Logistic Regression (LOGREG)", "Perceptron algorithm", and "PEGASOS" but does not specify their version numbers or the versions of any underlying programming languages or libraries. |
| Experiment Setup | Yes | The hyperparameters of the learning methods are chosen based on the performance of 2S. We tuned the hyperparameters in a wide range of values which we report in Appendix D. |