IEEE Computational Intelligence Magazine - August 2021 - 5

Haibo He, University of Rhode Island, USA
Jon Garibaldi, University of Nottingham, UK
Carlos A. Coello Coello, CINVESTAV-IPN, MEXICO
Julian Togelius, New York University, USA
Yaochu Jin, University of Surrey, UK
Yew Soon Ong, Nanyang Technological University, SINGAPORE
Hussein Abbass, University of New South Wales, AUSTRALIA
Spotlight
Publication
CIS Publication Spotlight
IEEE Transactions on Neural
Networks and Learning Systems
A Comprehensive Survey on Graph
Neural Networks, by Z. Wu, S. Pan, F.
Chen, G. Long, C. Zhang, and P. S.
Yu, IEEE Transactions on Neural Networks
and Learning Systems, Vol. 32,
No. 1, January 2021, pp. 4-24.
Digital Object Identifier: 10.1109/
TNNLS.2020.2978386
" Deep learning has revolutionized
many machine learning tasks in recent
years, ranging from image classification
and video processing to speech recognition
and natural language understanding.
The data in these tasks are typically represented
in the Euclidean space. However,
there is an increasing number of applications,
where data are generated from nonEuclidean
domains and are represented as
graphs with complex relationships and
interdependency between objects. The
complexity of graph data has imposed significant
challenges on the existing
machine learning algorithms. Recently,
many studies on extending deep learning
approaches for graph data have emerged.
In this article, we provide a comprehensive
overview of graph neural networks
(GNNs) in data mining and machine
learning fields. We propose a new taxonomy
to divide the state-of-the-art GNNs
into four categories, namely, recurrent
GNNs, convolutional GNNs, graph autoencoders,
and spatial-temporal GNNs. We
further discuss the applications of GNNs
Digital Object Identifier 10.1109/MCI.2021.3084390
Date of current version: 15 July 2021
IEEE Transactions on
Fuzzy Systems
©SHUTTERSTOCK.COM/BEERENTOCHTER
across various domains and summarize
the open-source codes, benchmark data
sets, and model evaluation of GNNs.
Finally, we propose potential research
directions in this rapidly growing field. "
A Survey of the Usages of Deep Learning
for Natural Language Processing, by
D. W. Otter, J. R. Medina, and J. K.
Kalita, IEEE Transactions on Neural
Networks and Learning Systems,
Vol. 32, No. 2, February 2021, pp.
604-624.
Digital Object Identifier: 10.1109/
TNNLS.2020.2979670
" Over the last several years, the field
of natural language processing has been
propelled forward by an explosion in the
use of deep learning models. This article
provides a brief introduction to the field
and a quick overview of deep learning
architectures and methods. It then sifts
through the plethora of recent studies
and summarizes a large assortment of
relevant contributions. Analyzed research
areas include several core linguistic processing
issues in addition to many applications
of computational linguistics. A
discussion of the current state of the art
is then provided along with recommendations
for future research in the field. "
An Effective Multiresolution Hierarchical
Granular Representation Based Classifier
Using General Fuzzy Min-Max Neural
Network, by T. T. Khuat, F. Chen,
and B. Gabrys, IEEE Transactions on
Fuzzy Systems, Vol. 29, No. 2, February
2021, pp. 427-441.
Digital Object Identifier: 10.1109/
TFUZZ.2019.2956917
" Motivated by the practical demands
for simplification of data toward being
consistent with human thinking and
problem-solving, as well as tolerance of
uncertainty, information granules are
becoming important entities in data processing
at different levels of data abstraction.
This article proposes a method to
construct classifiers from multiresolution
hierarchical granular representations using
hyperbox fuzzy sets. The proposed
approach forms a series of granular inferences
hierarchically through many levels
of abstraction. An attractive characteristic
of our classifier is that it can maintain a
high accuracy in comparison to other
fuzzy min-max models at a low degree of
granularity based on reusing the knowledge
learned from lower levels of abstraction.
In addition, our approach can
reduce the data size significantly as well as
handle the uncertainty and incompleteness
associated with data in real-world
applications. The construction process of
the classifier consists of two phases. The
first phase is to formulate the model at the
greatest level of granularity, while the later
stage aims to reduce the complexity of
AUGUST 2021 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 5
http://www.SHUTTERSTOCK.COM/BEERENTOCHTER
IEEE Computational Intelligence Magazine - August 2021

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - August 2021
