THEME AND SCOPE
One of the fundamental goals in computational intelligence is to achieve brain-like intelligence, a remarkable property of which is the ability to incrementally learn from noisy and incomplete data, and ability to adapt to changing environments. The special session aims at presenting novel approaches to incremental learning and adaptation to dynamic environments both from the more traditional and theoretical perspective of computational intelligence and from the more practical and application-oriented one.

This Special Session aspires at building a bridge between academic and industrial research, providing a forum for researchers in this area to exchange new ideas with each other, as well as with the rest of the neural network & computational intelligence community.

TOPICS
Papers must present original work or review the state-of-the-art in the following non-exhaustive list of topics:
. Methodologies/algorithms/techniques for learning in dynamic/non-stationary environments
. Incremental learning, lifelong learning, cumulative learning
. Domain adaptation and covariate-shift adaptation
. Semi-supervised learning methods for nonstationary environments
. Ensemble methods for learning in nonstationary environments
. Learning under concept drift and class imbalance
. Learning recurrent concepts
. Change-detection and anomaly-detection algorithms
. Information-mining algorithms in nonstationary data streams
. Cognitive-inspired approaches for adaptation and learning
. Applications that call for learning in dynamic/non-stationary environments, or change/anomaly detection, such as
o adaptive classifiers for concept drift
o adaptive/Intelligent systems
o fraud detection
o fault detection and diagnosis
o network-intrusion detection and security
o intelligent sensor networks
o time series analysis
. Benchmarks/standards for evaluating algorithms learning in non-stationary/dynamic environments


PAPER SUBMISSION
THE DEADLINE FOR THE PAPER SUBMISSION TO THE SPECIAL SESSION IS THE SAME OF IJCNN 2017, November 15th 2017.

All the submissions will be peer-reviewed with the same criteria used for other contributed papers.

Perspective authors will submit their papers through the IJCNN 2017 conference submission system at http://www.ijcnn.org/
Please make sure to select the Special Session nr 9 "Concept Drift, Domain Adaptation & Learning in Dynamic Environments" from the "S. SPECIAL SESSION TOPICS" name in the "Main Research topic" dropdown list;

Templates and instruction for authors will be provided on the IJCNN webpage http://www.ijcnn.org/

All papers submitted to the special sessions will be subject to the same peer-review procedure as regular papers, accepted papers will be published in the IEEE Conference Proceedings .

Further information about IJCNN 2017 can be found at http://www.ijcnn.org/

For any question you may have about the Special Session or paper submission, feel free to contact Giacomo Boracchi (boracchi@elet.polimi.it)

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Special Session on
"Concept Drift, Domain Adaptation & Learning in Dynamic Environments" @ IEEE IJCNN 2017

*Organizers*
. Giacomo Boracchi (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy)
. Robi Polikar (Rowan University, Glassboro, NJ, USA)
. Manuel Roveri (Politecnico di Milano, Dipartimento di Elettronica, Informazione e Bioingegneria, Italy)
. Gregory Ditzler, (University of Arizona, AZ, USA)

*TPC*
. Alfred Bifet, University of Waikato, New Zealand
. Gianluca Bontempi, Université Libre de Bruxelles, Belgium
. Giovanni Da San Martino, Qatar Computing Research Institute
. Barbara Hammer, Bielefeld University, Germany
. Georg Krempl, University Magdeburg, Germany
. Ludmilla Kuncheva, University of Bangor, Wales, UK
. Vincent Lemaire, Orange Labs, France
. Leandro L. Minku, University of Leicester, England, UK
. Russel Pears, Auckland University of Technology, New Zealan
. Leszek Rutkowski, Czestochowa University of Technology, Poland
. Shiliang Sun, East China Normal University, China
. Marley Vellasco, Pontifícia Universidade Católica do Rio de Janeiro, Brasil

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