We advance the theory of aggregation operators and introduce non-monotone aggregation methods based on minimization of a penalty for inputs disagreements. The application in mind is processing data sets which may contain noisy values. Our aim is to filter out noise while at the same time preserve signs of unusual values. We review various methods of robust estimators of location, and then introduce a new estimator based on penalty minimisation.
Field of Research
080108 Neural, Evolutionary and Fuzzy Computation 080201 Analysis of Algorithms and Complexity
Socio Economic Objective
970108 Expanding Knowledge in the Information and Computing Sciences
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