Measures of ruleset quality for general rules extraction methods

The paper deals with quality measures of whole sets of rules extracted from data, as a counterpart to more commonly used measures of individual rules. It sketches the typology of rules extraction methods and of their rulesets, and recalls that quality measures for whole sets of rules have been so fa...

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Bibliographic Details
Published in:International journal of approximate reasoning 2009-06, Vol.50 (6), p.867-879
Main Author: Holena, M
Format: Article
Language:English
Subjects:
Fuzzy logic
Observational logic
Quality measures
ROC curves
Rules extraction from data
Ruleset measures
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Summary:The paper deals with quality measures of whole sets of rules extracted from data, as a counterpart to more commonly used measures of individual rules. It sketches the typology of rules extraction methods and of their rulesets, and recalls that quality measures for whole sets of rules have been so far used only in the case of classification rulesets. Then three particular approaches to extending ruleset quality measures from classification to general rulesets are discussed. The paper also recalls the possibility to measure the dependence of classification rulesets on parameters of the classification method by means of ROC curves, and proposes a generalization of ROC curves to general rulesets. Finally, the approach is illustrated on rulesets extracted with four important rules extraction methods from the well-known iris data.
ISSN:0888-613X
1873-4731
DOI:10.1016/j.ijar.2009.03.002