Fuzzy measure-based aggregations allow taking interactions among coalitions of the input
sources into account. Their main drawback when applying them in real-world problems …

We review recent literature on three aspects of fuzzy measures: their representations,
learning optimal fuzzy measures and random generation of various types of fuzzy measures …

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 …

The use of aggregation operators that require ordering of the data brings a problem when
the structures to be aggregated are multi-valued, since there may be several admissible …

The main aim in ensemble learning is using multiple classifiers rather than one classifier to
aggregate classifiers' outputs for more accurate classification. Generating an ensemble …

Feature Selection (FS) is an effective preprocessing method to deal with the curse of
dimensionality in machine learning. Redundant features in datasets decrease the …

The Choquet integral, defined with respect to a capacity, also known as a non-additive set
function or fuzzy measure, constitutes a versatile class of aggregation operators …

Hierarchical classification has been previously demonstrated to yield more ac-curate results
in comparison to flat classifiers; however, the tree-like structure intro-duces the problem of …

The design of an ensemble of classifiers involves the definition of an aggregation
mechanism that produces a single response obtained from the information provided by the …