Description:

Data fusion is crucial in almost every application of artificial intelligence.  Classification, image processing, decision-making, big data, or deep learning require collecting and fusing data in appropriate ways to solve specific problems. For this reason, a huge effort is devoted to the development and analysis of data fusion methods, especially in the context of imperfect and uncertain information.

Aggregation functions are one of the most widely used methods in this sense. They are defined as monotone functions with appropriate boundary conditions and include, among others, most of the means or functions such as the product, the minimum, or the maximum.

However, in recent years it has been shown that the concept of aggregation function can be too restrictive, as it does not cover some examples that can provide good results in particular applications, as is the case of the mode. Furthermore, some data fusion functions are more general than aggregation functions. For example, the so-called pre-aggregation functions have been proposed to deal with problems ranging from classification to the computational brain, with promising results.

This special issue focuses in mathematical foundations, models and techniques for data fusion for Artificial Intelligence under uncertainty, aiming at disclosing the most recent and innovate developments in the field, including, but not limited to:

- Theoretical results in aggregation functions, pre-aggregation functions, and fusion functions with other kinds of weaker monotonicity; 

- Theoretical results in common aggregation functions, pre-aggregation functions, and fusion functions on many-valued status  (including, e.g. lattice-valued) 

- Theoretical results in the controlling of the uncertainty in interval-valued data fusion; 

- Other fusion functions, models and techniques for data fusion under uncertainty;

- (Adaptative) Neuro-fuzzy models and systems;

- Deep (fuzzy) learning;

- Fuzzy data stream;

- Applications in decision making (including, e.g. multi-criteria decision making), image processing, classification and multi-label classification, machine learning, data stream clustering, and data flow prediction.