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”.
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Kacprzyk, J. (1997). OWA Operators in Machine Learning from Imperfect Examples. In: Yager, R.R., Kacprzyk, J. (eds) The Ordered Weighted Averaging Operators. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6123-1_24
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DOI: https://doi.org/10.1007/978-1-4615-6123-1_24
Publisher Name: Springer, Boston, MA
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