IEEE Computational Intelligence Magazine - May 2023 - 14

IEEE Transactions on Fuzzy Systems
Fuz-Spam: Label Smoothing-Based
Fuzzy Detection ofSpammers in Internet
ofThings, by Z.Guo, K. Yu, A.Jolfaei,
F. Ding, N. Zhang, IEEE Transactions
on Fuzzy Systems, Vol. 30, No. 11,
Nov. 2022, pp. 4543-4554.
Digital Object Identifier: 10.1109/
TFUZZ.2021.3130311
" Nowadays, online spamming has
already been a remarkable threat to
contents security of Internet of Things.
Due to constant technical progress,
online spamming activities have been
more and more concealed. This brings
much fuzziness to spammer detection
scenarios, yielding the issue of fuzzy
detection of spammers. Although existing
detection techniques for spammers
utilized idea of deep learning, they still
ignore to release power of label spaces.
As real nature about a user may be usually
fuzzy, but the label annotated for a
user is always certain. To remedy such
gap, this article proposes a label smoothing-based
fuzzy detection method for
spammers (Fuz-Spam). First of all, deep
representation is still utilized to deeply
fuse features, which acts as the foundation
of neural computing. On this basis,
generative adversarial learning is introduced
to transform previous label spaces
into distributed forms. In addition, two
groups of experiments are carried out
on two real-world datasets for evaluation.
The results demonstrate that the
Fuz-Spam improves identification efficiency
about 10% to 20% than previous
ones, and that the Fuz-Spam is
endowed with proper stability. "
Fuzzy Deep Forest With Deep Contours
Featurefor LeafCultivar Classification, by
W. Zheng, L. Yan, C. Gou, and F. Y.
Wang, IEEE Transactions on Fuzzy
Systems, Vol. 30, No. 12, Dec. 2022,
pp. 5431-5444.
Digital Object Identifier: 10.1109/
TFUZZ.2022.3177764
" Deep learning is a compelling technique
for feature extraction due to its
adaptive capacity ofprocessing and providing
deeper image information. However,
for the task ofleafcultivar classification, the
deep learning-based classifier model is
unable to extract contour features of leaf
images deeply due to the lack oflarge specialized
datasets and expert knowledge
annotations. Also, the scale/size ofthe current
leafcultivar dataset does not meet the
needs ofdeep neural networks (DNNs). In
particular, the high model complexity of
DNNsimpliesthatdeep-learning-based
neural networks seem to must require a
large dataset to achieve good performance,
but facing the fact that the leaf cultivar
dataset often is small, even some classes in
this kind of datasets contain less than ten
images/examples. To overcome these
problems and inspired by the resounding
success of fuzzy logic, we propose a novel
fuzzy ensemble model for leafcultivar classification.
To extract the contours of
leaves, we first propose generative adversarial
networks-based methods. Second, to
improve the ability of feature representation,
we present a data augmentation
method to transform our contour features.
Third, to get the essential features ofleaves,
we design a novel generation ofthe fuzzy
random forest. Finally, to achieve accurate
classification, we design a novel deep learning
strategy, namely deep fuzzy representation
learning, integrating and cascading a
lot of our fuzzy random forests. Experimental
results show that our model outperforms
other existing state-of-the-art on
three real-world datasets, and performs
much better than the original deep forest
andDNN-based algorithms particularly. "
IEEE Transactions on Evolutionary
Computation
Automated Configuration of Genetic
Algorithms by Tuning for Anytime Performances,
by F. Ye, C. Doerr, H.
Wang, andT. B€ack, IEEETransactions
on Evolutionary Computation,Vol.26,
No. 6, Dec. 2022, pp. 1526-1538.
Digital Object Identifier: 10.1109/
TEVC.2022.3159087
" Finding the best configuration of
algorithms' hyperparameters for a given
14 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2023
optimization problem is an important task
in evolutionary computation. We compare
in this work the results offour different
hyperparameter optimization (HPO)
approaches for a family of genetic algorithms
(GAs) on 25 diverse pseudo-Boolean
optimization (PBO) problems. More
precisely, we compare previously obtained
results from a grid search with those
obtained from three automated configuration
techniques: 1) iterated racing; 2)
mixed-integer parallel-efficient global
optimization (MIP-EGO); and 3) mixedinteger
evolutionary strategies. Using two
different cost metrics: 1) expected running
time (ERT) and 2) the area under the
empirical cumulative distribution function
(ECDF) curve, we find that in several cases
the best configurations with respect to
ERT are obtained when using the area
under the ECDF curve as the cost metric
during the configuration process. Our
results suggest that even when interested in
ERT performance, it might be preferable
to use anytime performance measures for
the configuration task. We also observe
that tuning for ERT is much more sensitive
with respect to the budget that is allocated
to the target algorithms. "
IEEE Transactions on Games
Toward Designer Modeling Through
Design Style Clustering,byA.Alvarez,J.
Font, and J. Togelius, IEEE Transactions
on Games,Vol.14,No.4,Dec.
2022, pp. 676-686.
Digital Object Identifier: 10.1109/
TG.2022.3143800
" We propose modeling designer style
in mixed-initiative game content creation
tools as archetypical design traces. These
design traces are formulated as transitions
between design styles; these design styles
are in turn found through clustering all
intermediate designs along the way to
making a complete design. This method
is implemented in the EvolutionaryDungeon
Designer, a research platform for
mixed-initiative systems to create adventure
and dungeon crawler games. We
present results both in the form ofdesign
styles for rooms, which can be analyzed
IEEE Computational Intelligence Magazine - May 2023

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