Discrete fuzzy integrals and their applications

This doctoral thesis introduces new generalizations of discrete fuzzy integrals with three distinct applications. We introduce four new families of discrete fuzzy integrals, including the d-XChoquet integral, the dCF -integrals, and the extended families of discrete Choquet and Sugeno integrals. These new generalizations are designed to provide better flexibility and performance in applications. In addition, we investigate the theoretical properties of these new integral generalizations, such as monotonicity. Furthermore, extensive experiments are conducted to compare the performance of the new, proposed, integrals with existing ones in three distinct applications: in classification, decision-making analysis, and in brain signal processing. The results demonstrate that the proposed generalizations perform better than other discrete fuzzy integrals in most applications.