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 (for August 2015 Issue)
15th November, 2014: Submission of Manuscripts
15th January, 2015: Notification of Review Results
15th Faburary, 2015: Submission of Revised Manuscripts
15th March, 2015: Submission of Final Manuscripts
August 2015: Publication
Guest Editors
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
