IEEE Computational Intelligence Magazine - May 2021 - 12

denote as Bouncing NotMNIST, and
Penn Treebank. Notably, our approach
can, in some instances, outperform full
BPTT as well as variants such as sparse
attentive backtracking. Significantly,
the hidden unit correction phase of
P-TNCN allows it to adapt to new
data sets even if its synaptic weights are
held fixed (zero-shot adaptation) and
facilitates retention of prior generative
knowledge when faced with a task
sequence. We present results that show
the P-TNCN's ability to conduct zeroshot adaptation and online continual
sequence modeling. "
IEEE Transactions on
Fuzzy Systems

A New Approach for TransformationBased Fuzzy Rule Interpolation, by T.
Chen, C. Shang, J. Yang, F. Li, and
Q. Shen, IEEE Transactions on Fuzzy
Systems, Vol. 28, No. 12, December
2020, pp. 3330-3344.
Digital Object Identifier: 10.1109/
TFUZZ.2019.2949767
" Fuzzy rule interpolation (FRI) is
of particular significance for reasoning
in the presence of insufficient knowledge or sparse rule bases. As one of
the most popular FRI methods, transformation-based fuzzy rule interpolation (TFRI) works by constructing an
intermediate fuzzy rule, followed by
running scale and move transformations. The process of inter mediate
rule construction selects a user-defined
number of rules closest to an observation that does not match any existing rule, using a distance metric. It
relies upon heur istically computed
weights to assess the contribution of
individual selected rules. This process requires a m ove operation in
an effort to force the inter mediate
rule to overlap with an unmatched
observation, regardless of what rules
are selected and how much contribution they may each make. It is, therefore, desirable to avoid this problem
and also to improve the automation of
rule interpolation without resorting to
the user's intervention for fixing the

number of closest rules. This article
proposes such a novel approach to
selecting a subset of rules from the
sparse rule base with an embedded
rule weighting scheme for the automatic assembling of the intermediate
rule. Systematic comparative experimental results are provided on a
range of benchmark datasets to
demonstrate statistically significant
improvement in the perfor mance
achieved by the proposed approach
over that obtainable using conventional TFRI. "
SFCM: A Fuzzy Clustering Algorithm
of Extracting the Shape Information of
Data, by Q-T. Bui, B. Vo, V. Snasel,
W. Pedrycz, T-P. Hong, N-T.
Nguyen, and M-Y. Chen, IEEE
Transactions on Fuzzy Systems, Vol.
29, No. 1, January 2021, pp. 75-89.
Digital Object Identifier: 10.1109/
TFUZZ.2020.3014662
" Topological data analysis is a new
theoretical trend using topological techniques to mine data. This approach
helps determine topological data structures. It focuses on investigating the
global shape of data rather than on
local information of high-dimensional
data. The Mapper algorithm is considered as a sound representative
approach in this area. It is used to
cluster and identify concise and
meaningful global topological data
structures that are out of reach for
many other clustering methods. In
this article, we propose a new method
called the Shape Fuzzy C-Means
(SFCM) algorithm, which is constructed based on the Fuzzy C-Means algorithm with particular features of the
Mapper algorithm. The SFCM algorithm can not only exhibit the same
clustering ability as the Fuzzy C-Means
but also reveal some rel ation ships
through visualizing the global shape of
data supplied by the Mapper. We present a formal proof and include experiments to confirm our claims. The
performance of the enhanced algorithm is demonstrated through a comparative analysis involving the original

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | MAY 2021

algor ithm, Mapper, and the other
fuzzy set based improved algorithm,
F-Mapper, for synthetic and realwo r l d d a t a . T h e c o m p a r i s o n is
conducted with respect to output visualization in the topological sense and
clustering stability. "
IEEE Transactions on
Evolutionary Computation

A Hybrid Deep Grouping Algorithm for
Large Scale Global Optimization, by
H. Liu, Y. Wang, and N. Fan, IEEE
Transactions on Evolutionary Computation, Vol. 24, No. 6, December 2020,
pp. 1112-1124.
Digital Object Identifier: 10.1109/
TEVC.2020.2985672
" Many real-world problems contain
a large number of decision variables
which can be modeled as large scale
global optimization (LSGO) problems.
One effective way to solve an LSGO
problem is to decompose it into smaller
subproblems to solve. The existing
works mainly focused on designing
methods to decompose separable problems, while seldom focused on the
decomposition of nonseparable large
scale problems. Also, the existing
decomposition methods only learn the
interaction (correlation or interdependence) among variables to make the
decomposition. In this article, we make
the decomposition deeper: we not only
consider the variable interaction but
also take the essentialness of the va--
riable into account to form a deep
grouping method. To do this, we first
design an essential/trivial var iable
detection scheme to support the deep
decomposition for both separable problems and nonseparable problems. Based
on it, we propose a new decomposition
method called deep grouping method.
Then, we design a new differential evolution (DE) algorithm with a new
mutation strategy.
By integrating all these, we propose a
hybrid deep grouping (HDG) algorithm. Finally, the experiments are conducted on the widely used and most
challenging LSGO benchmark suites,

IEEE Computational Intelligence Magazine - May 2021

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