A comparison of the linguistic Choquet and Sugeno fuzzy integrals

One of the popular fuzzy set theoretic technologies is the fuzzy integral. It combines numeric confidence from information sources with the worth of subsets of those sources. But sometimes, we encounter data that has a higher order of uncertainty, e.g., confidence values, locations, etc., that can only be expressed in linguistic terms. It is our belief that dealing with this type of data through the extended version of the regular algorithms produces meaningful analyses. The regular algorithms are extended using the extension principle. While we have reported success in applying the extended Choquet integral on landmine detection problems, there is a need to study the behavior of the linguistic fuzzy integrals. In this paper, we examine the extended version of the Choquet and Sugeno fuzzy integrals on a synthetic data set to gain insight into the effect of varying conditions on the final results.