Approximation by filter functions
This work provides a theoretical unification for researchers in evidence theory, rough sets, and probability, but it is incremental as it builds on existing concepts without new empirical results.
The paper identifies a common formal framework among estimators like belief functions, rough set approximations, and contextual probability, unifying them as filter functions composed of basic probability and variable weighting. It concludes with a simulation study in item response theory to compare these filter functions.
In this exploratory article, we draw attention to the common formal ground among various estimators such as the belief functions of evidence theory and their relatives, approximation quality of rough set theory, and contextual probability. The unifying concept will be a general filter function composed of a basic probability and a weighting which varies according to the problem at hand. To compare the various filter functions we conclude with a simulation study with an example from the area of item response theory.