OU Portal
Log In
Welcome
Applicants
Z6_60GI02O0O8IDC0QEJUJ26TJDI4
Error:
Javascript is disabled in this browser. This page requires Javascript. Modify your browser's settings to allow Javascript to execute. See your browser's documentation for specific instructions.
{}
Close
Publikační činnost
Probíhá načítání, čekejte prosím...
publicationId :
tempRecordId :
actionDispatchIndex :
navigationBranch :
pageMode :
tabSelected :
isRivValid :
Record type:
stať ve sborníku (D)
Home Department:
Ústav pro výzkum a aplikace fuzzy modelování (94410)
Title:
Probabilistic-Fuzzy Inference with Piecewise Linear Quantile Regression
Citace
Cao, T. H. N., Holčapek, M., Valášek, R. a Madrid, N. M. Probabilistic-Fuzzy Inference with Piecewise Linear Quantile Regression.
In:
The 22nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2025: Modeling Decisions for Artificial Intelligence 2025-09-15 València.
Cham: Springer, 2025. s. 214-226. ISBN 978-3-032-00891-6.
Subtitle
Publication year:
2025
Obor:
Number of pages:
13
Page from:
214
Page to:
226
Form of publication:
Elektronická verze
ISBN code:
978-3-032-00891-6
ISSN code:
0302-9743
Proceedings title:
Modeling Decisions for Artificial Intelligence
Proceedings:
Mezinárodní
Publisher name:
Springer
Place of publishing:
Cham
Country of Publication:
Sborník vydaný v zahraničí
Název konference:
The 22nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2025
Conference venue:
València
Datum zahájení konference:
Typ akce podle státní
příslušnosti účastníků:
Celosvětová akce
WoS code:
EID:
2-s2.0-105013622666
Key words in English:
Piecewise linear quantile regression; Probabilistic-Fuzzy inference system; Quantile fuzzy transform; Weighted quantile regression
Annotation in original language:
Rule-based systems, particularly those using "IF antecedents THEN consequent" rules, play a crucial role in mathematical modeling and decision-making. This work focuses on a specific type of rule-based system: the Probabilistic-Fuzzy Inference System. Here, antecedents are represented as fuzzy sets, while consequents are modeled as probability distributions using quantile functions. The inference process relies on the $L_{1}$-Fuzzy transform, also known as the Quantile Fuzzy transform. For each fuzzy antecedent, a weighted quantile of order $p$ is computed, and the inverse quantile transform derives the empirical quantile function for any input value. Initially, weighted quantiles are modeled as scalar values. To better capture dependencies between input and output data, we extend this model to a piecewise linear function, developed in a structured manner with a comprehensive computational algorithm. Its effectiveness is demonstrated through an illustrative example.
Annotation in english language:
Rule-based systems, particularly those using "IF antecedents THEN consequent" rules, play a crucial role in mathematical modeling and decision-making. This work focuses on a specific type of rule-based system: the Probabilistic-Fuzzy Inference System. Here, antecedents are represented as fuzzy sets, while consequents are modeled as probability distributions using quantile functions. The inference process relies on the $L_{1}$-Fuzzy transform, also known as the Quantile Fuzzy transform. For each fuzzy antecedent, a weighted quantile of order $p$ is computed, and the inverse quantile transform derives the empirical quantile function for any input value. Initially, weighted quantiles are modeled as scalar values. To better capture dependencies between input and output data, we extend this model to a piecewise linear function, developed in a structured manner with a comprehensive computational algorithm. Its effectiveness is demonstrated through an illustrative example.
References
Reference
R01:
Complementary Content
Deferred Modules
${title}
${badge}
${loading}
Deferred Modules