Abstract
In this paper we propose an extension of the notion of deviation-based aggregation function tailored to aggregate multidimensional data. Our objective is both to improve the results obtained by other methods that try to select the best aggregation function for a particular set of data, such as penalty functions, and to reduce the temporal complexity required by such approaches. We discuss how this notion can be defined and present three illustrative examples of the applicability of our new proposal in areas where temporal constraints can be strict, such as image processing, deep learning and decision making, obtaining favourable results in the process.
•We have expanded the target of deviation-based functions to the multivariable case.•We provide a new construction method for this type of deviation-based functions.•Our approach is not restricted to aggregating values of the unit interval.•Temporal complexity is lower than that of alternatives such as penalty functions.•Our method can be applied in image reduction, deep learning or decision making.