2007 | OriginalPaper | Buchkapitel
Measures of Ruleset Quality Capable to Represent Uncertain Validity
verfasst von : Martin Holeňa
Erschienen in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
The paper deals with quality measures of rules extracted from data, more precisely with measures of the whole extracted rulesets. Three particular approaches to extending ruleset quality measures from classification to general rulesets are discussed, and one of them, capable to represent uncertain validity of rulesets for objects, is elaborated in some detail. In particular, a generalization of ROC curves is proposed. The approach is illustrated on rulesets extracted with four important methods from the well-known iris data.