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 that 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=ieeecimcdbil2015
IMPORTANT (REVISED) DATES (for November 2015 Issue)
1st Feb, 2015: Submission of Manuscripts
15th April, 2015: Notification of Review Results
15th May, 2015: Submission of Revised Manuscripts
15th June, 2015: Submission of Final Manuscripts
GUEST EDITORS
Professor Amir Hussain,
University of Stirling, Stirling FK9 4LA, Scotland, UK
Email:
ahu@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@uts.edu.au
Professor Jonathan Wu
University of Windsor, 401 Sunset Avenue, Windsor, ON, Canada
Email:
jwu@uwindsor.ca
Professor Dongbin Zhao
Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
E-mail:
dongbin.zhao@gmail.com
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A PDF copy of the CFP is attached with this email for forwarding to
interested colleagues. It is also available for download from:
http://www.cs.stir.ac.uk/~ahu/IEEE-CIM-CICE2015.pdf
For more information on the
IEEE CIM, see:
http://cis.ieee.org/ieee-computational-intelligence-magazine.html