IEEE International Conference on Fuzzy Systems
Vancouver, Canada, 25-29 July, 2016
Special Session on Fuzzy-based methods for machine learning: data preprocessing, learning models and their applications
Title: Fuzzy-based methods for machine learning: data preprocessing, learning models and their applications.
Organizers: Mikel Galar, Public University of Navarre, Pamplona, Spain; Bartosz Krawczyk, Wroclaw University of Technology, Poland; Isaac Triguero, Ghent University, Belgium;
Submission Deadline: 15th January 2016
Notification Acceptance: 15th March 2016
Final paper submission deadline: 15th April 2016
You should follow the FUZZ-IEEE 2016 Submission Web Site. Special session papers are treated the same as regular conference papers.
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
Multi-label \ Multi-instance learning
Feature and label noise
Kernels and Support Vector Machines
Evolutionary fuzzy systems
One-class classification / Learning from positive and unlabeled samples
Real-world applications e.g., in medical informatics, bioinformatics, social networks, biometry, etc.
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 Navarre, Pamplona, Spain. He is currently an assistant professor in the Department of Automatics and Computation at the Public University of Navarre. He is the author of 24 published original articles in international journals and 30 contributions to conferences. He is also reviewer of more than 30 international journals. His research interests are data-mining, classification, multi-classification, ensemble learning, evolutionary algorithms, fuzzy systems and fingerprint recognition. He is a member of the European Society for Fuzzy Logic and Technology (EUSFLAT) and the Spanish Association of Artificial Intelligence (AEPIA).
Bartosz Krawczyk received the M.Sc. and Ph.D. degrees in Computer Science from the Wroclaw University of Technology, Poland, in 2012 and 2015, respectively. He currently works at the Department of Systems and Computer Networks at the same university. His research is focused on machine learning, multiple classifier systems, one-class classifiers, class imbalance, data streams and interdisciplinary applications of these methods. He has published 26 international journal papers and over 75 contributions to conferences. Dr Krawczyk was awarded with numerous prestigious awards for his scientific achievements like PRELUDIUM and ETIUDA grants from Polish National Science Center, Scholarship of Polish Minister of Science and Higher Education or START award from Foundation for Polish Science among others. He served as a Guest Editor in four journal special issues devoted to ensemble learning and data stream classification and organized eight special sessions devoted to these topics at prestigious conferences such as European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD) or IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE). He is a member of Program Committee for over 40 international conferences and serves as a reviewer for 20 international journals.
Isaac Triguero received the M.Sc. and Ph.D. degrees in Computer Science from the University of Granada, Granada, Spain, in 2009 and 2014, respectively. He is currently post-doctoral researcher at the Inflammation Research Center of the Ghent University, Ghent, Belgium. He has published 22 international journal papers as well as more than 20 contributions to conferences. His research interests include data mining, data reduction, biometrics, evolutionary algorithms, semi-supervised learning and big data learning.
Name: Mikel Galar
Email address: firstname.lastname@example.org
Affiliation: Public University of Navarra
Postal address: Department of Automatics and Computations, Public University of Navarre, 31006 Pamplona, Spain
Telephone number: +34 948 166040
Name: Bartosz Krawczyk
Email address: email@example.com
Affiliation: Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology
Postal address: Wybrzeze Wyspianskiego 27,50-370 Wroclaw,Poland
Telephone number: +48 692 979 578
Name: Isaac Triguero
Email address: Isaac.Triguero@irc.vib-UGent.be
Affiliation: Inflammation Research Center, a VIB-UGent Department UGent Department of Internal Medicine, Respiratory Medicine (GE01)
Postal address: Technologiepark 927, B-9052 Zwijnaarde, Belgium.
Telephone number: +32(0)9 331 37 45