IEEE Computational Intelligence Magazine - November 2020 - 6

IEEE Transactions on
Fuzzy Systems

Fast and Scalable Approaches to Accelerate the Fuzzy k-Nearest Neighbors
Classifier for Big Data, by J. Maillo, S.
García, J. Luengo, F. Herrera, and I.
Triguero, IEEE Transactions on Fuzzy
Systems, Vol. 28, No. 5, May 2020,
pp. 874-886.
Digital Object Identifier: 10.1109/
TFUZZ.2019.2936356
"One of the best-known and most
effective methods in supervised classifi-
cation is the k-nearest neighbors algo-
rithm (kNN). Several approaches have
been proposed to improve its accuracy,
where fuzzy approaches prove to be
among the most successful, highlighting
the classical fuzzy k-nearest neighbors
(FkNN). However, these traditional
algorithms fail to tackle the large
amounts of data that are available today.
There are multiple alternatives to en--
able kNN classification in big datasets,
spotlighting the approximate version of
kNN called hybrid spill tree. Neverthe-
less, the existing proposals of FkNN for
big data problems are not fully scalable,
because a high computational load is
required to obtain the same behavior as
the original FkNN algorithm. This
article proposes global approximate
hybrid spill tree FkNN and local
hybrid spill tree FkNN, two approxi-
mate approaches that speed up runtime
without losing quality in the classifica-
tion process. The experimentation com-
pares various FkNN approaches for big
data with datasets of up to 11 million
instances. The results show an improve-
ment in runtime and accuracy over lit-
erature algorithms."
Multitasking Genetic Algor ithm
(MTGA) for Fuzzy System Optimization, by D. Wu and X. Tan, IEEE
Transactions on Fuzzy Systems, Vol. 28,
No. 6, June 2020, pp. 1050-1061.
Digital Object Identifier: 10.1109/
TFUZZ.2020.2968863
"Multitask learning uses auxiliary
data or knowledge from relevant tasks to

6

facilitate the learning in a new task.
Multitask optimization applies multitask
learning an optimization to study how
effectively and efficiently tackle the
multiple optimization problems, simulta-
neously. Evolutionary multitasking, or
multi-factorial optimization, is an
emerging subfield of multitask optimi-
zation, which integrates evolutionary
computation and multi-task learning.
This article proposes a novel and easyto-implement multitasking genetic algo-
rithm (MTGA), which copes well with
significantly different optimization tasks
by estimating and using the bias among
them. Comparative studies with eight
state-of-the-art single-task and multitask
approaches in the literature on nine
benchmarks demonstrated that, on aver-
age, the MTGA outperformed all of
them and had lower computational cost
than six of them. Based on the MTGA,
a simultaneous optimization strategy for
fuzzy system design is also proposed.
Experiments on simultaneous optimiza-
tion of type-1 and interval type-2 fuzzy
logic controllers for couple-tank water
level control demonstrated that the
MTGA can find better fuzzy logic con-
trollers than other approaches."
IEEE Transactions on Evolutionary
Computation

Feature Extraction and Selection for Parsimonious Classifiers With Multiobjective
Genetic Programming, by K. Nag and
N. R. Pal, IEEE Transactions on Evolutionary Computation, Vol. 24, No. 3,
June 2020, pp. 454-466.
Digital Object Identifier: 10.1109/
TEVC.2019.2927526
"The objectives of this paper are to
investigate the capability of genetic pro-
gramming to select and extract linearly
separable features when the evolutionary
process is guided to achieve the same
and to propose an integrated system for
that. It decomposes a c-class problem
into c binary classification problems and
evolve c sets of binary classifiers employ-
ing a steady-state multi-objective genet-
ic programming with three minimizing
objectives. Each binary classifier is com-

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2020

posed of a binary tree and a linear sup-
port vector machine (SVM). The
features extracted by the feature nodes
and some of the function nodes of the
tree are used to train the SVM. The
decision made by the SVM is consid-
ered as the decision of the correspond-
ing classifier. During crossover and
mutation, the SVM-weights are used to
determine the usefulness of the corre-
sponding nodes. It also uses a fitness
function based on Golub's index to
select useful features. To discard less fre-
quently used features, it employs unfit-
ness functions for the feature nodes. The
method is compared with 34 classifica-
tion systems using 18 datasets. The per-
formance of the proposed method is
found to be better than 432 out of 570,
i.e., 75.79% of comparing cases."
IEEE Transactions on Games

Winning Is Not Everything: Enhancing
Game Development With Intelligent
Agents, by Y. Zhao, I. Borovikov, F.
de Mesentier Silva, A. Beirami, J.
Rupert, C. Somers, J. Harder, J.
Kolen, J. Pinto, R. Pourabolghasem,
J. Pestrak, H. Chaput, M. Sardari, L.
Lin, S. Narravula, N. Aghdaie, and
K. Zaman, IEEE Transactions on
Games,Vol. 12, No. 2, June 2020, pp.
199-212
Digital Object Identifier: 10.1109/
TG.2020.2990865
"Recently, there have been several
high-profile achievements of agents
learning to play games against humans
and beat them. In this article, we study
the problem of training intelligent
agents in service of game development.
Unlike the agents built to "beat the
game," our agents aim to produce
human-like behavior to help with game
evaluation and balancing. We discuss two
fundamental metrics based on which we
measure the human-likeness of agents,
namely skill and style, which are multi-
faceted concepts with practical implica-
tions outlined in this article. We report
four case studies in which the style and
skill requirements inform the choice of
algorithms and metrics used to train
IEEE Computational Intelligence Magazine - November 2020

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