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In this work, we introduce the notion of d G -Choquet integral, which generalizes the discrete Choquet integral replacing, in the first place, the difference between inputs represented by closed subintervals of the unit interval [ 0 , 1 ] by a dissimilarity function; and we also replace the sum by more general appropriate functions. We show that particular cases of d G -Choquet integral are both the discrete Choquet integral and the d -Choquet integral. We define interval-valued fuzzy measures and we show how they can be used with d G -Choquet integrals to define an interval-valued discrete Choquet integral which is monotone with respect to admissible orders. We finally study the validity of this interval-valued Choquet integral by means of an illustrative example in a classification problem. [ABSTRACT FROM AUTHOR]

The paper introduces a new class of functions from 0 , 1 n to 0 , n called d -Choquet integrals. These functions are a generalization of the "standard" Choquet integral obtained by replacing the difference in the definition of the usual Choquet integral by a dissimilarity function. In particular, the class of all d -Choquet integrals encompasses the class of all "standard" Choquet integrals but the use of dissimilarities provides higher flexibility and generality. We show that some d -Choquet integrals are aggregation/pre-aggregation/averaging/functions and some of them are not. The conditions under which this happens are stated and other properties of the d -Choquet integrals are studied. [ABSTRACT FROM AUTHOR]