Conditional Relative Frequency Distributions with Undefined Observations and Generalized Fuzzy Orthopartitions
Abstract
Conditional relative frequency distributions are tools extensively employed in statistics and machine learning for analyzing connections of two or more categorical variables, examining patterns, and comparing data. As a first goal, we introduce the so-called conditional relative frequency distributions with undefined observations for representing frequencies characterized by uncertainty. After that, we show that conditional relative frequency distributions with undefined observations can be identified with particular generalized fuzzy orthopartitions, which are mathematical models describing vague partitions where the membership of elements to classes is only partially known.
Cite
Text
Boffa and Ciucci. "Conditional Relative Frequency Distributions with Undefined Observations and Generalized Fuzzy Orthopartitions." Journal of Artificial Intelligence Research, 2025. doi:10.1613/JAIR.1.18020Markdown
[Boffa and Ciucci. "Conditional Relative Frequency Distributions with Undefined Observations and Generalized Fuzzy Orthopartitions." Journal of Artificial Intelligence Research, 2025.](https://mlanthology.org/jair/2025/boffa2025jair-conditional/) doi:10.1613/JAIR.1.18020BibTeX
@article{boffa2025jair-conditional,
title = {{Conditional Relative Frequency Distributions with Undefined Observations and Generalized Fuzzy Orthopartitions}},
author = {Boffa, Stefania and Ciucci, Davide},
journal = {Journal of Artificial Intelligence Research},
year = {2025},
doi = {10.1613/JAIR.1.18020},
volume = {83},
url = {https://mlanthology.org/jair/2025/boffa2025jair-conditional/}
}