OWA Operators in Machine Learning from Imperfect Examples

Janusz Kacprzyk ()
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Janusz Kacprzyk: Polish Academy of Sciences ul., Systems Research Institute

A chapter in The Ordered Weighted Averaging Operators, 1997, pp 321-329 from Springer

Abstract: Abstract We show how Yager’s (1988) ordered weighted averaging (OWA) operators can be employed in (inductive) learning from examples which are assumed to be imperfect in the sense of errors, misclassifications, classifications to a degree, etc. We formulate the problem as to find a concept decription covering, say, almost all of the positive examples and almost none of the negative examples. Thus, by neglecting some examples, those errors are somehow “masked”.

Keywords: inductive learning; learning from examples; fuzzy logic; fuzzy lingustic quantifier; OWA operator (search for similar items in EconPapers)
Date: 1997
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4615-6123-1_24

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DOI: 10.1007/978-1-4615-6123-1_24

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