Measures of Ruleset Quality Capable to Represent Uncertain Validity

DOI10.1007/978-3-540-75256-1_39zbMath1148.68435OpenAlexW2167260029MaRDI QIDQ3524950

Martin Holeňa

Publication date: 16 September 2008

Published in: Lecture Notes in Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-75256-1_39

zbMATH Keywords

classificationfuzzy rulesROC curvesquality measuresobservational logicrules extraction from dataBoolean rules

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (1)

Measures of ruleset quality for general rules extraction methods

Uses Software

• ANFIS
• C4.5
• LISp-Miner
• NEFCLASS-X

Cites Work

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• Metamathematics of fuzzy logic
• Fuzzy data analysis with NEFCLASS
• ROC `n' rule learning -- towards a better understanding of covering algorithms
• Data mining and knowledge discovery with evolutionary algorithms
• Formal logics of discovery and hypothesis formation by machine
• Fuzzy hypotheses testing in the framework of fuzzy logic
• Mining pure linguistic associations from numerical data
• Piecewise-Linear Neural Networks and Their Relationship to Rule Extraction from Data
• Detecting group differences: Mining contrast sets

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