TY - JOUR
T1 - Guest Editorial Special Issue on Emerging Computational Intelligence Techniques for Decision Making with Big Data in Uncertain Environments
AU - Dingr, Weiping
AU - Pal, Nikhil R.
AU - Lin, Chin Teng
AU - Cheung, Yiu Ming
AU - Cao, Zehong
AU - Luo, Wenjian
N1 - Funding Information:
In summary, six selected papers for this special issue highlight a subset of the challenging and novel applications of emerging computational intelligence theories and methodologies for decision making with big data in an uncertain environment. The guest editors would like to thank all the authors who submitted their work to the special issue, and all reviewers for their hard work in completing timely and constructive reviews. Special thanks go to the Editor-in-Chief, Prof Yew-Soon Ong, and members of the editorial team for their support during the editing process of this Special Issue. They worked closely with the guest editors to ensure excellent quality of this issue and guarantee its success. To carry out this work, some of the Guest Editors are supported in part by the National Natural Science Foundation of China under Grant 61300167 and Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20151274 and Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and sponsored by Qing Lan Project of Jiangsu Province.
PY - 2021/2
Y1 - 2021/2
N2 - The papers in this special section focus on emerging computational intelligent techniques for decision making with Big Data in uncertain environments. Decision making in a big-data environment poses many challenges because of the high dimensional, heterogeneous, complex, unstructured, and unpredictable characteristics of the data which often suffer from different kinds of uncertainty. The uncertainty in the data may arise due to many factors including missing values, imprecise measurements, changes in process characteristics during the data generation period, lack of appropriate monitoring of data measurement process to name a few. Internet-of-Things (IoT) systems usually generate a large amount of unstructured and heterogeneous data demanding specialized techniques for data analytics. Thus, decision making in such an environment poses significant challenges and often demands new and innovative design techniques and algorithms for decision making.
AB - The papers in this special section focus on emerging computational intelligent techniques for decision making with Big Data in uncertain environments. Decision making in a big-data environment poses many challenges because of the high dimensional, heterogeneous, complex, unstructured, and unpredictable characteristics of the data which often suffer from different kinds of uncertainty. The uncertainty in the data may arise due to many factors including missing values, imprecise measurements, changes in process characteristics during the data generation period, lack of appropriate monitoring of data measurement process to name a few. Internet-of-Things (IoT) systems usually generate a large amount of unstructured and heterogeneous data demanding specialized techniques for data analytics. Thus, decision making in such an environment poses significant challenges and often demands new and innovative design techniques and algorithms for decision making.
UR - http://www.scopus.com/inward/record.url?scp=85100304248&partnerID=8YFLogxK
U2 - 10.1109/TETCI.2021.3049701
DO - 10.1109/TETCI.2021.3049701
M3 - Editorial
AN - SCOPUS:85100304248
SN - 2471-285X
VL - 5
SP - 2
EP - 5
JO - IEEE Transactions on Emerging Topics in Computational Intelligence
JF - IEEE Transactions on Emerging Topics in Computational Intelligence
IS - 1
ER -