IEEE Computational Intelligence Magazine Special Issue on Computational Intelligence for Changing Environments
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 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
enviroments. 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
enviroments 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 enviroments,
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 enviroments
algorithms and architectures, theoretical foundations, and
representations, and their application to real-world problems. We also
welcome timely surveys and 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
- Incremental Unsupervised and semi-supervised learning architectures
- Incremental Incremental Representation learning and disentangling
- Incremental Knowledge augmentation
- Incremental Adaptive Neuro-fuzzy systems
- Incremental and single-pass data mining
- Incremental Neural Clustering
- Incremental Neural 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://www.easychair.org/conferences/?conf=ieeecimcdbil2015.
Important Dates
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
November 2015: Publication
Guest Editor
Professor Amir Hussain
University of Stirling
Stirling FK9 4LA SCOTLAND, UK
Email:
ahu@cs.stir.ac.uk
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,
No. 95, Zhongguancun East Road, Beijing 100190, China
E-mail:
dongbin.zhao@gmail.com
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