Connectionists: Call for Papers: IEEE Computational Intelligence Magazine (CIM) Special Issue: "Computational Intelligence for Changing Environments"

[Dr Amir Hussain]

Call for Papers (Please also forward to any interested colleagues -
with advance apologies for any cross-postings!)

IEEE Computational Intelligence Magazine (CIM)
(http://cis.ieee.org/ieee-computational-intelligence-magazine.html)

Special Issue on "Computational Intelligence for Changing
Environments"  (Submission Deadline: 15 Nov 2014)
(http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf)

Guest Editors: Amir Hussain, Dacheng Tao, Jonathan Wu and Dongbin Zhao

Aims and Scope:

Over the past decade or so, computational intelligence techniques have
been highly successful for solving big data challenges in changing
environments. In particular, there has been growing interest in so
called biologically inspired learning (BIL), which refers to a wide
range of learning techniques, motivated by biology, that try to mimic
specific biological functions or behaviors. Examples include the
hierarchy of the brain neocortex and neural circuits, which have
resulted in biologically-inspired features for encoding, deep neural
networks for classification, and spiking neural networks for general
modelling.

To ensure these models are generalizable to unseen data, it is common
to assume that the training and test data are independently sampled
from an identical distribution, known as the sample i.i.d. assumption.
In dynamic and non-stationary environments,  the distribution of data
changes over time, resulting in the phenomenon of ‘concept drift’
(also known as population drift or concept shift), which is a
generalization of covariance shift in statistics. Over the last five
years, transfer learning and multitask learning have been used to
tackle this problem. Fundamental analyses using probably approximately
correct (PAC) and Rademacher complexity frameworks have  explained why
appropriate incorporation of context and concept drift can improve
generalizability in changing environments.

It is possible to use human-level processing power to tackle concept
drift in changing environments. Concept drift is a real-world problem,
usually associated with online and concept learning, where the
relationships between input data and target variables dynamically
change over time. Traditional learning schemes do not adequately
address this issue, either because they are offline or because they
avoid  dynamic learning. However, BIL seems to possess properties that
would be helpful for solving concept drift problems in changing
environments. Intuitively, the human capacity to deal with concept
drift is innate to cognitive processes, and the learning problems
susceptible to concept drift seem to share some of the dynamic demands
placed on plastic neural areas in the brain. Using improved biological
models in neural networks can provide insight into cognitive
computational phenomena.

However, a main outstanding issue in using computational intelligence
for changing environments and domain adaptation is how to build
complex networks, or how networks should be connected to the features,
samples, and distribution drifts. Manual design and building of these
networks are beyond current human capabilities. Recently,
computational intelligence methods has been used to address concept
drift in changing environments, with promising results. A Hebbian
learning model has been used to handle random, as well as correlated,
concept drift. Neural networks have been used for concept drift
detection, and the influence of latent variables on concept drift in a
neural network has been studied. In another study, a timing-dependent
synapse model has been applied to concept drift. These works mainly
apply biologically-plausible computational models to concept drift
problems. Although these results are still in their infancy, they open
up new possibilities to achieve brain-like intelligence for solving
concept drift problems in changing environments.

Taking the current state of research in computational intelligence for
changing environments into account, the objective of this special
issue is to collate this research to help unify the concepts and
terminology of computational intelligence in changing environments,
and to survey state-of-the-art computational intelligence
methodologies and the key techniques investigated to date. Therefore,
this special issue invites submissions on the most recent developments
in computational intelligence for changing environments algorithms and
architectures, theoretical foundations, and representations, & their
application to real world problems. We also welcome timely surveys &
review papers.

Topics of Interest include (but are not limited to):
• Computational intelligence methodologies and implementation for
changing environments
• Transfer learning, Multitask learning, Domain adaption
• Incremental Learning architectures, Unsupervised and semi-supervised
learning architectures
• Incremental Knowledge augmentation, Representation learning and disentangling
• Incremental Adaptive Neuro-fuzzy systems
• Incremental and single-pass data mining
• Incremental Neural Clustering & Regression
• Incremental Adaptive decision systems
• Incremental Feature selection and reduction
• Incremental Constructive Learning
• Novelty detection in Incremental learning

Submission Process
The maximum length for the manuscript is typically 25 pages in single
column format with double-spacing, including figures and references.
Authors should specify in the first page of their manuscripts the
corresponding author’s contact and up to 5 keywords. Submission should
be made via: https://easychair.org/conferences/?conf=ieee-cim-cice2015

Important Dates (for August 2015 Issue)
15th November, 2014: Submission of Manuscripts
15th January, 2015: Notification of Review Results
15th February, 2015: Submission of Revised Manuscripts
15th March, 2015: Submission of Final Manuscripts

Guest Editors
Professor Amir Hussain,
University of Stirling,
Stirling FK9 4LA, Scotland UK
Email: ahu at cs.stir.ac.uk
http://cs.stir.ac.uk/~ahu/

Professor Dacheng Tao,
University of Technology,
Sydney, 235 Jones Street,
Ultimo, NSW 2007, Australia
Email: dacheng.tao at uts.edu.au

Professor Jonathan Wu
University of Windsor
401 Sunset Avenue, Windsor,
ON, Canada
Email: jwu at uwindsor.ca

Professor Dongbin Zhao
Institute of Automation, Chinese
Academy of Sciences,
Beijing 100190, China
E-mail: dongbin.zhao at gmail.com

-- 
The University of Stirling has been ranked in the top 12 of UK universities for graduate employment*.
94% of our 2012 graduates were in work and/or further study within six months of graduation.
*The Telegraph
The University of Stirling is a charity registered in Scotland, number SC 011159.