Computational Intelligence - August 2014 - 13

Databases authored by Huijse et al.,
discusses a specific yet important Big
Data application where CI techniques
can play an essential role, namely time
domain astronomy (TDA). The paper
indicates that astronomical time series
analysis is characterized by not only a
huge size, but also highly uneven and
noisy samples, and imbalance in different sample classes. As a result, unsupervised, semi-supervised and active
learning techniques have been widely
adopted for solving different problems
in TDA. Then, the Large Synoptic
Survey Telescope is presented as an
example for discussing Big Data challenges in the context of TDA. Such
challenges, as also seen in other typical
Big Data problems range from large

dimensionality, noise, heterogeneity to
computationally efficiency.
Both papers are of review nature discussing the opportunities and promises
of CI techniques along with challenges.
They have raised more questions than
answers, which is actually the aim of this
Special Issue to trigger a wider range of
interests in the CI community.
Finally, we would like to thank Prof.
Hisao Ishibuchi, the Editor-in-Chief,
for giving us the opportunity to guestedit this special issue. We are equally
grateful to all authors who submitted
their work to this special issue and
reviewers for providing us timely and
insightful reviews, without which it
would not have been possible for us to
produce the Special Issue.

References

[1] T. Hey, S. Tansley, and K. Tolle, Eds., The Fourth Paradigm: Data-Intensive Scientific Discovery. Washington, D.C.:
Microsoft Research, Raymond, 2009.
[2] Z. Yang, K. Tang, and X. Yao, "Large scale evolutionary optimization using cooperative coevolution," Inform.
Sci., vol. 178, no. 15, pp. 2985-2999, 2008.
[3] H. Ishibuchi, N. Tsukamoto, and Y. Nojima, "Evolutionary many-objective optimization: A short review," in Proc.
IEEE Congr. Evolutionary Computation, 2008, pp. 2424-2431.
[4] R. Polikar and C. Alippi, "Guest editorial: Learning in
non-stationary and evolving environments," IEEE Trans.
Neural Netw. Learn. Syst., vol. 25, no. 1, pp. 9-11, Jan. 2014.
[5] E. Cambria, B. White, T. S. Durrani, and N. Howard,
"Guest editorial: Computational intelligence for natural
language processing," IEEE Comput. Intell. Mag., vol. 9,
no. 1, pp. 19-63, Feb. 2014.
[6] E. Cambria and B. White, "Jumping NLP curves: A
review of natural language processing research," IEEE
Comput. Intell. Mag., vol. 9, no. 2, pp. 48-57, May 2014.
[7] S. A. Thomas and Y. Jin, "Reconstructing biological
gene regulatory networks: Where optimization meets big
data," Evol. Intell., vol. 7, no. 1, pp. 29-47, 2014.
[8] [Online]. Available: http://ieee-ssci.org/CIBD.html

Call for Papers for Journal Special Issues
Special Issue on "Neurodynamic Systems for Optimization and Applications"
Journal: IEEE Transactions on Neural Networks and Learning Systems
Guest Editors: Zhigang Zeng, Andrzej Cichocki, Long Cheng, Youshen Xia, Xiaolin Hu
Submission Deadline: August 15, 2014
Publication: May/June 2015
Further Information: Long Cheng (chenglong@compsys.ia.ac.cn)
http://compsys.ia.ac.cn/~chenglong/ndo.pdf
Special Issue on "New Trends of Learning in Computational Intelligence"
Journal: IEEE Computational Intelligence Magazine
Guest Editors: Guang-Bin Huang, Erik Cambria, Kar-Ann Toh, Bernard Widrow, Zongben Xu
Submission Deadline: August 15, 2014
Publication: May 2015
Further Information: Guang-Bin Huang (egbhuang@ntu.edu.sg)
http://www.ntu.edu.sg/home/egbhuang/pdf/CIM-2015-05(New-Trends)-Final.pdf
Digital Object Identifier 10.1109/MCI.2014.2326258

AUGUST 2014 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

http://www.ieee-ssci.org/CIBD.html http://compsys.ia.ac.cn/~chenglong/ndo.pdf http://www.ntu.edu.sg/home/egbhuang/pdf/CIM-2015-05(New-Trends)-Final.pdf

Table of Contents for the Digital Edition of Computational Intelligence - August 2014