Measures of ruleset quality for general rules extraction methods

DOI10.1016/j.ijar.2009.03.002zbMath1191.68679OpenAlexW2162784348MaRDI QIDQ962905

Martin Holeňa

Publication date: 7 April 2010

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2009.03.002

zbMATH Keywords

fuzzy logicROC curvesquality measuresobservational logicrules extraction from dataruleset measures

Mathematics Subject Classification ID

Logic in artificial intelligence (68T27) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (1)

OPTIMONOTONE MEASURES FOR OPTIMAL RULE DISCOVERY

Uses Software

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

Cites Work

• Mechanizing hypothesis formation. Mathematical foundations for a general theory
• Metamathematics of fuzzy logic
• Fuzzy data analysis with NEFCLASS
• Triangular norms
• 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
• Machine learning-based receiver operating characteristic (ROC) curves for crisp and fuzzy classification of DNA microarrays in cancer research
• A rough set approach for the discovery of classification rules in interval-valued information systems
• Mining pure linguistic associations from numerical data
• Piecewise-Linear Neural Networks and Their Relationship to Rule Extraction from Data
• Measures of Ruleset Quality Capable to Represent Uncertain Validity
• Detecting group differences: Mining contrast sets
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