Aggregation functions are key operators in the Fuzzy Sets theory. In this field, advances from the theoretical and the applied points of view have been steady in the last decades. Traditional aggregation functions such as the weighted mean are now known as particular cases of more general families such as the Choquet integral. Other important families are t-norms, t-conorms, uninorms, OWAs, etc. These operators have proved to be useful in responding to a growing need to handle uncertainty and large amounts of data and the associated challenges that arise.

Logical connectives, especially fuzzy implication functions, play an important role in the Fuzzy Sets theory. In addition to its theoretical importance, in recent years there has been a growing interest in the applications of these operators in fields as diverse as image processing, data mining, control theory, approximate reasoning, etc.

Fuzzy implication functions and aggregation functions are intimately related since several families of aggregation functions have been used successfully in the generation of new classes of fuzzy implication functions and vice versa. Moreover, functional equations where both operators appear are common.

This special session is devoted to the theoretical and applied aspects of aggregation functions and logical connectives, especially fuzzy implication functions. General topics include, but are not limited to:

• Aggregation functions: theory and applications.
• Fuzzy implication functions: theory and applications.
• Relationship between fuzzy aggregation and fuzzy implication functions.

Decision making is a common activity in several disciplines and research areas (Engineering, Social Sciences, Health Sciences, Artificial Intelligence, etc.). This session focuses on environments where the objectives, restrictions or  alternatives associated with decision-making processes are imprecise, i.e., decision-making processes under uncertainty.

The objective of this special session is to provide an opportunity to discuss and disseminate the most recent theoretical and empirical advances in this field.as.

Resolution of classification and regression problems is a very active research field due to the vast number of real world problems dealing with these type of problems. Furthermore, there are currently a lot of hot topics embraced within these problems as the imbalanced data, learning with low quality data, multi-label or multi-instance problems and one-class problems among others.

Learning methods based on soft computing techniques are widely used to cope with the previously mentioned problems. Fuzzy systems have proven to provide accurate results using models that can be interpreted by human beings. Evolutionary computation is a robust technique to tackle optimization and learning tasks. Their usage allows one to adapt the parameters of the model for each problem such that an accurate system can be obtained. The synergy between these two techniques usually implies an enhancement of the design and optimization of fuzzy systems (or other paradigms like neural networks).

Objectives and topics

The aim of the session is to provide a forum to disseminate and discuss recent and significant research efforts on soft computing techniques to deal with classification and regression problems. The special session is therefore open to high quality submissions from researchers working in this research field. Contributions of this special session should be original research works and innovative results in areas including, but not limited to, the following ones:

• Fuzzy rule-based systems
• Evolutionary fuzzy systems
• Neural networks
• Clustering
• Semi-supervised learning
• Unsupervised learning
• Soft computing applications in learning
• Multi-label \ Multi-instance learning
• Quantification problems
• Ensemble learning
• Problems with low quality data and noise
• Imbalanced learning
• Interpretable models
• Cost sensitive problems
• One-class classification

Short biography of the organizers

Mikel Galar received the M.Sc. and Ph.D. degrees in Computer Science in 2009 and 2012, both from the Public University of Navarra, Pamplona, Spain. He is currently an assistant professor at the Department of Automatic and Computation at the Public University of Navarre. He is the author of 32 published original articles in international journals and more than 45 contributions to conferences. His research interests are data mining, classification, multi-classification, ensemble learning, evolutionary algorithms, fuzzy systems and big data. He is a member of the IEEE, the European Society for Fuzzy Logic and Technology (EUSFLAT) and the Spanish Association of Artificial Intelligence (AEPIA). He has received the extraordinary prize for his PhD thesis from the Public University of Navarre and the 2013 IEEE Transactions on Fuzzy System Outstanding Paper Award for the paper A New Approach to Interval-Valued Choquet Integrals and the Problem of Ordering in Interval-Valued Fuzzy Set Applications (bestowed in 2016). His research activity can be observed in Googlescholar Citations.

José Antonio Sanz received the M.Sc. degree in computer sciences and the Ph.D. degree, both form the Public University of Navarra in 2008 and 2011 respectively. He is currently an assistant professor with the Department of Automatics and Computation, Public University of Navarra. He is the author of 27 published original articles in international journals and 45 contributions to conferences. He received the best paper award in the FLINS 2012 international conference and the Pepe Millá award in 2014. His research interests include fuzzy techniques for classification problems, interval-valued fuzzy sets, genetic fuzzy systems, data mining and medical applications of soft computing techniques. Dr. Sanz is member of the European Society for Fuzzy Logic and Technology (EUSFLAT). His research activity can be observed in Googlescholar Citations.

Contact information:

Name: Mikel Galar
Email address: mikel.galar@unavarra.es
Affiliation: Public University of Navarre
Postal address: Department of Computer Science and Artificial Intelligence, Public University of Navarre, 30006 Pamplona, Spain
Telephone number: +34-948-166048

Name: José Antonio Sanz
Email address: joseantonio.sanz@unavarra.es
Postal address: Department of Computer Science and Artificial Intelligence, Public University of Navarre, 30006 Pamplona, Spain
Telephone number: +34-948-169265

This session intends to reunite research groups from the disciplines of Soft Computing (SC) and Natural Language Generation (NLG) to create a common ground where the discussion of the convergence between both fields takes place. In this sense, researchers are encouraged to submit papers related to topics within the scope of a cross-fertilization between SC and NLG. Pure NLG-oriented topics are very welcome as well.

The session is devoted to hold all those contributions that relate techniques or aspects of fuzzy logic with issues of the field of education. Among others, a list of possible topics for contributions in this session are indicated:

• Analysis of fuzzy concepts in order to reach a deep understanding of them.
• Strategies for introducing and/or using of fuzzy concepts in different disciplines.
• "Type" lessons related to fuzzy logic.
• Computer tools based on fuzzy logic to improve the teaching / learning process.
• Computer tools for the teaching of fuzzy concepts or fuzzy strategies.
• New evaluation techniques based on fuzzy data or fuzzy information.

This special session will deal with applications where aggregation functions and pre-aggregation functions play a crucial role. These applications may include, but are not limited to,

• image processing,
• data mining,
• big data
• classification,
• decision making,
• deep learning.

The considered applications may make use either of real aggregation functions or of more general aggregation functions, as those defined over lattices or extensions of fuzzy sets or of generalized forms of aggregation functions such as pre-aggregation functions. We hope to provide a forum to discuss up-to-date applications in which the usefulness of recent developments in aggregation and pre-aggregation functions is clear.

Aggregation theory is a field in rapid progress. Apart from their obvious interest from a theoretical point of view, aggregation functions are an essential tool for applications in many different fields, such as image processing, classification, machine learning or decision making among many other. For this reason, in recent years there is a growing interest in the topic, which are leading to the introduction of novel concepts and approaches. Among them, it is remarkable the notion of pre-aggregation functions, which extends the classical notion of aggregation function by relaxing the monotonicity conditions.
In this special session , we will focus on recent theoretical developments in the fields of aggregation functions and pre-aggregation functions, in order to discuss the most recent developments and the recent trends in this area. In particular, interest topics include, but are not limited to:

• t-norms and t-conorms,
• overlap and grouping functions,
• means,
• uninorms,
• any type of aggregation function both in the real setting and in lattices,
• Pre-aggregation functions
• Extensions of the notion of aggregation function
• Choquet and Sugeno integrals and their generalization-

Many computational intelligence and learning methods, including expert systems, fuzzy logic, neural networks, genetic algorithm, artificial immune networks, swarm particle techniques, ACO, reinforcement learning, etc., have gained successful applications in many control automation fields. Intelligent Control, which is distinguished from conventional approaches since it is historically based on methodologies borrowed from Artificial Intelligence, mainly Soft Computing techniques, has been proved able to cope with problems – especially industrial ones – where conventional methods were reputed less efficient or unsuccessful.
Applications of Intelligent Control may include without restrictions: transportation systems, medical, biomedical and biological systems, aerospace, automation, biotechnology, mechatronics, manufacturing, process control, power systems, energy and smart grid, agriculture, environmental systems, robotics and autonomous systems, economics and business systems, etc. In recent years, a trend has emerged that has proved that in the industrial world and in some other fields related to real engineering applications these intelligent techniques, mainly fuzzy and neuro-fuzzy, play an important role.
This special session aims to disseminate high quality research results regarding developments in integration of computational intelligence theories and control techniques, mainly in automation, related to effective applications to real and complex systems.
The topics include (but are not limited to) the following ones:

• Intelligent control: fuzzy control, neuro-control, neuro-fuzzy, hybrid techniques.
• Optimization by heuristic techniques in system engineering and control.
• Modelling and identification by intelligent techniques.
• Engineering application of intelligent computation.
• Applications in industry, energy, aerospace, marine, aerial systems, etc.
• Other related topics.