IEEE Computational Intelligence Magazine - February 2023 - 18

(FWAdaBoost)-based ensemble system
with a self-organizing fuzzy inference
system (SOFIS) as the ensemble component
is proposed. To better incorporate
the SOFIS, FWAdaBoost utilizes
the confidence scores produced by the
SOFIS in both sample weight updating
and ensemble output generation, resulting
in more accurate classification
boundaries and greater prediction precision.
Numerical examples on a wide
range of benchmark classification problems
demonstrate the efficacy of the
proposed approach. "
Fuzzy Rule Interpolation With KNeighbors
for TSK Models, by P.
Zhang, C. Shang, and Q. Shen,
IEEE Transactions on Fuzzy Systems,
Vol. 30, No. 10, Oct 2022, pp.
4031-4043.
Digital Object Identifier: 10.1109/
TFUZZ.2021.3136359
" When a fuzzy system is presented
with an incomplete (or sparse) rule base,
fuzzy rule interpolation (FRI) offers a
useful mechanism to infer conclusions for
unmatched observations. However, most
existing FRI methodologies are established
for Mamdani inference models,
but not for Takagi-Sugeno-Kang (TSK)
ones. This article presents a novel
approach for computing interpolated
outcomes with TSKmodels, using only a
small number ofneighboring rules to an
unmatched observation. Compared with
existing methods, the new approach
helps improve the computational efficiency
ofthe overall interpolative reasoning
process, while minimizing the
adverse impact on accuracy induced by
firing those rules of low similarities with
the new observation. For problems that
involve a rule base of a large size, where
closest neighboring rules may be rather
alike to one another, a rule-clusteringbased
method is introduced. It derives an
interpolated conclusion by first clustering
rules into different groups with a clustering
algorithm and then, by utilizing only
those rules that are each selected from
one of a given, small number of closest
rule clusters. Systematic experimental
examinations are carried out to verify the
efficacy of the introduced techniques, in
comparison with state-of-the-art methods,
over a range of benchmark regression
problems, while employing different
clustering algorithms (which also shows
the flexibility in ways of implementing
the novel approach). "
IEEE Transactions on Evolutionary
Computation
An Approximated Gradient Sign
Method Using Differential Evolution for
Black-Box Adversarial Attacks,byC.
Li, H. Wang, J. Zhang, W. Yao, and
T. Jiang, IEEE Transactions on Evolutionary
Computation, Vol. 26, No. 5,
Oct. 2022, pp. 976-990.
Digital Object Identifier: 10.1109/
TEVC.2022.3151373
" Recent studies show that deep
neural networks are vulnerable to
adversarial attacks in the form of subtle
perturbations to the input image, which
leads the model to output wrong prediction.
Such an attack can easily succeed
by the existing white-box attack
methods, where the perturbation is calculated
based on the gradient of the
target network. Unfortunately, the gradient
is often unavailable in the realworld
scenarios, which makes the
black-box adversarial attack problems
practical and challenging. In fact, they
can be formulated as high-dimensional
black-box optimization problems at the
pixel level. Although evolutionary algorithms
are well known for solving
black-box optimization problems, they
cannot efficiently deal with the highdimensional
decision space. Therefore,
we propose an approximated gradient
sign method using differential evolution
(DE) for solving black-box adversarial
attack problems. Unlike most existing
methods, it is novel that the proposed
method searches the gradient sign rather
than the perturbation by a DE algorithm.
Also, we transform the pixelbased
decision space into a dimension18
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2023
reduced decision space by combining
the pixel differences from the input
image to neighbor images, and two different
techniques for selecting neighbor
images are introduced to build the
transferred decision space. In addition,
six variants of the proposed method are
designed according to the different
neighborhood selection and optimization
search strategies. Finally, the performance
of the proposed method is
compared with a number of the stateof-the-art
adversarial attack algorithms
on CIFAR-10 and ImageNet datasets.
The experimental results suggest that
the proposed method shows superior
performance for solving black-box
adversarial attack problems, especially
nontargeted attack problems. "
IEEE Transactions on Games
Beyond Genre: Classifying Virtual
Reality Experiences, by M. Foxman,
D. Beyea, A. P. Leith, R. A. Ratan,
V. H. H. Chen and B. Klebig, IEEE
Transactions on Games, Vol. 14, No.
3, Sep. 2022, pp. 466-477.
Digital Object Identifier: 10.1109/
TG.2021.3119521
" Because virtual reality (VR) shares
common features with video games, consumer
content is usually classified according
to traditional game genres and
standards. However, VR offers different
experiences based on the medium's
unique affordances. To account for this
disparity, the article presents a comparative
analysis oftitles from the Steam digital
store across three platform types: VR
only, VR supported, and non-VR. We
analyzed data from a subset of the most
popular applications within each category
(N ¼ 141, 93, and 1217, respectively).
The three classification types we analyzed
were academic game genres, developer
defined categories, and user-denoted
tags. Results identify the most common
content classifications (e.g., Action and
Shooter within VR only applications),
the relative availability of each between
platforms (e.g., Casual is more common
IEEE Computational Intelligence Magazine - February 2023

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - February 2023
