Abstract
We discuss the problem of measuring the quality of decision support (classification) system that involves granularity. We put forward the proposal for such quality measure in the case when the underlying granular system is based on rough and fuzzy set paradigms. We introduce the notion of approximation, loss function, and empirical risk functional that are inspired by empirical risk assessment for classifiers in the field of statistical learning.
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Szczuka, M. (2011). Risk Assessment in Granular Environments. In: Peters, J.F., Skowron, A., Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Transactions on Rough Sets XIII. Lecture Notes in Computer Science, vol 6499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18302-7_8
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DOI: https://doi.org/10.1007/978-3-642-18302-7_8
Publisher Name: Springer, Berlin, Heidelberg
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