An Equivalence Analysis of Binary Quantification Methods
Authors: Alberto Castaño, Jaime Alonso, Pablo González, Juan José del Coz
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | After an empirical evaluation of all these methods using synthetic and benchmark datasets, the paper concludes recommending three of them due to their precision, efficiency, and diversity. |
| Researcher Affiliation | Academia | Alberto Casta no, Jaime Alonso, Pablo Gonz alez, Juan Jos e del Coz Artificial Intelligence Center University of Oviedo. Campus de Viesques, 33204, Gij on, Spain uo226218@uniovi.es, jalonso@uniovi.es, gonzalezgpablo@uniovi.es, juanjo@uniovi.es |
| Pseudocode | Yes | Algorithm 1: Mixture function used by QUANTy |
| Open Source Code | Yes | This study1 compares six of the quantification algorithms previously discussed: AC, HDy, PAC, QUANTy, SORD, and MM (renamed as CDFy) and has two goals. (...) 1github.com/bertocast/binary-quantification-equivalence |
| Open Datasets | Yes | After an empirical evaluation of all these methods using synthetic and benchmark datasets, the paper concludes recommending three of them due to their precision, efficiency, and diversity. (...) The second group of experiments was carried out using benchmark datasets. |
| Dataset Splits | Yes | Following (Forman 2008), 50-fold CV was used to estimate the training distributions. (...) The RF hyperparameters (depth, number of trees and minimum number of examples for the leaf nodes) were automatically adjusted using a grid search and 3fold cross-validation optimizing the geometric mean to obtain adequate classifiers even when classes were unbalanced. All the quantifiers were trained over the same partitions, 70% for training and 30% for testing, with 40 repetitions. |
| Hardware Specification | Yes | The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. |
| Software Dependencies | No | The paper mentions 'Logistic Regression' and 'Random Forest' as base learners, but does not provide specific version numbers for these libraries or any other key software components, such as Python or PyTorch/TensorFlow versions. |
| Experiment Setup | Yes | Logistic Regression with C =1 was employed to train the binary classifiers. (...) The RF hyperparameters (depth, number of trees and minimum number of examples for the leaf nodes) were automatically adjusted using a grid search and 3fold cross-validation optimizing the geometric mean to obtain adequate classifiers even when classes were unbalanced. |