Concept Drift Special Issue on IEEE Transactions on Neural Networks and Learning Systems

[Robi Polikar]

Dear Colleagues:


Great news: Our special issue proposal on “Learning in Nonstationary
and Evolving Environments” has been approved by IEEE Transactions on
Neural Networks and Learning Systems. I hope you will be able to
contribute your recent work, as you have been working on topics that
fall well within the scope of this special issue. The submission
deadline is 15 April 2012, which I hope provides ample time for
preparing the manuscript. I am including the call for papers (CFP )
below for your reference. Please also allow me to apologize in advance
for multiple postings: you are likely to receive multiple copies of
this CFP, as we will provide the announcement to all relevant mailing
lists to ensure that all interested researchers are informed about
this special issue.

Warmest regards,

Robi and Cesare

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
SPECIAL ISSUE ON LEARNING IN NONSTATIONARY AND EVOLVING ENVIRONMENTS

Using a computational model to learn under various environments has
been a well-researched field that pro-duced relevant results;
unfortunately, the majority of these efforts rely on three fundamental
assumptions: i) there is a sufficient and representative data set to
configure and assess the model performance; ii) data are drawn from a
fixed – albeit unknown – distribution; and iii) samples are mostly
supposed to be independent. Alas, all these assumptions often do not
hold in many real-world applications, such as in the analysis of
climate or financial data, network intrusion, spam and fraud
detection, electricity demand and industrial quality inspection among
many others. Recent efforts towards incremental and online learning
allow us to relax the “sufficiency” requirement by continuously
updating a model to learn from small batches or online data, yet, the
data that become available are still assumed to be drawn from a fixed
distribution. More recently, approaches commonly called concept drift
– and to some extend domain adaptation– algorithms, possibly in
collaboration with change detection tests, have attempted to remove
this assumption, by accommodating a stream or batches of data whose
underlying distribution changes over time. However, early efforts have
made other assumptions, such as restricting the type of faults or
changes affecting the system or the distribution and are primarily
heuristic in nature with several free parameters to be fine-tuned.
Against this background, the need for a general framework to learn
from – and adapt to – a changing environ-ment can be hardly
overstated. A special issue that discusses the state-of-the-art and
latest results on detecting and adapting to changes in underlying data
distributions is very timely.
We invite original and unpublished contributions in all areas relevant
to learning in a changing environment. Papers must present original
work or review the state-of-the-art in the following non-exhaustive
list of topics:
• Learning in non-stationary, drifting or dynamic environments
• Adaptive learning in a missing, faulty, limited or unbalanced data
context
• Incremental, lifelong and cumulative learning from nonstationary
data
• Faults, changes or anomaly detection in data streams
• Domain adaptation
• Data mining from streams of data
• Architectures, techniques and algorithms for learning in such
environments
• Applications requiring learning in dynamic and nonstationary
environments

IMPORTANT DATES
15 April 2012 – Deadline for manuscript submission
15 August 2012 – Notification of authors
15 September 2012 – Deadline for submission of revised manuscripts
30 September 2012 – Final decision
January/February 2013 – Special issue publication in the IEEE TNNLS

GUEST EDITORS
Prof. Cesare Alippi, Politecnico di Milano, Italy,
cesare...@polimi.it
Prof. Robi Polikar, Rowan University, USA, pol...@rowan.edu

SUBMISSION INSTRUCTIONS
1. Read the information for Authors at http://ieee-cis.org/pubs/tnn/papers/
2. Submit the manuscript at the IEEE-TNNLS webpage http://mc.manuscriptcentral.com/tnn
and follow the submission procedure. Please, clearly indicate on the
first page of the manuscript and the Author's Cover Letter that the
manuscript has been submitted to the Special Issue on Learning in
Nonstationary and Evolving Envi-ronments. Send also an email to the
guest editors to notify about your submission.