The aim of this special session is to serve as a forum for the exchange of ideas and discussions on recent and new trends regarding intersections between fuzzy systems and machine learning methods. Machine learning is a very active research field because of the huge number of real-world applications that can be addressed by this field of research. There are many contemporary problems, besides the canonical classification, regression or clustering, that require special focus and development of novel and efficient solutions. Such challenges include the problem of imbalanced data, learning on the basis of low quality and noisy examples, multi-label and multi-instance problems, or having limited access to object labels at the training phase, among others.

Learning methods based on Soft Computing techniques are widely used to face the aforementioned challenges with promising results. Fuzzy systems have demonstrated the ability to provide at the same time interpretable models understandable by human beings, as well as highly accurate results. Moreover, fuzzy-based techniques are of great interest when dealing with low quality or noisy data as they provide a framework to manage uncertainty. Evolutionary computation is a robust technique for optimization, learning and preprocessing tasks. They can adapt the model parameters for each problem to obtain a highly accurate system forming a good synergy with fuzzy approaches.

We encourage authors to submit original papers as well as preliminary and promising works in the topics of this special session.


Objectives and topics:

The aim of the session is to provide a forum for the exchange of ideas and discussions on Soft Computing techniques and algorithms for machine learning, in order to deal with the current challenges in this topic. The special session is therefore open to high quality submissions from researchers working in learning problems using soft computing techniques. The topics of this special session include fuzzy models for handling data-level difficulties and improving machine learning methods in areas such as:

Supervised / Unsupervised / Semi-supervised learning

Feature Selection / Extraction / Construction

Instance Selection / Generation

Data streams and concept drift

Big data mining

Imbalanced learning

Multi-label \ Multi-instance learning

Feature and label noise

Kernels and Support Vector Machines

Ensemble learning

Evolutionary fuzzy systems

One-class classification / Learning from positive and unlabeled samples

Manifold Learning

Real-world applications e.g., in medical informatics, bioinformatics, social networks, biometry, etc.



Organizers:
Mikel Galar, Public University of Navarre, Pamplona, Spain, mikel.galar@unavarra.es
Bartosz Krawczyk, Wroclaw University of Technology, Poland, bartosz.krawczyk@pwr.edu.pl
Isaac Triguero, Ghent University, Belgium, Isaac.Triguero@irc.vib-UGent.be

Paper Submission:

You should follow the FUZZ-IEEE 2016 Submission Web Site and select this special session as your paper topic. Special session papers are treated the same as regular conference papers.