IEEE Computational Intelligence Magazine - August 2020 - 13

Digital Object Identifier: 10.1109/
TFUZZ.2019.2900856
"Interpretability has always been a
major concern for fuzzy rule-based
classifiers. The usage of humanreadable models allows them to
explain the reasoning behind their
predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule
based classifiers have not been able
to maintain the good tradeoff
between accuracy and interpretability that has characterized these
techniques in non-Big-Data environments. The most accurate
methods build models composed
of a large number of rules and
fuzzy sets that are too complex,
while those approaches focusing
on interpretability do not provide
state-of-the-art discr imination
capabilities. In this paper, we propose a new distributed learning
algorithm named CFM-BD to
construct accurate and compact
fuzzy rule-based classification systems for Big Data. This method has
been specifically designed from
scratch for Big Data problems and
does not adapt or extend any existing algorithm. The proposed learning process consists of three stages:
Preprocessing based on the probability integral transform theorem;
rule induction inspired by CHIBD and Apriori algorithms; and
rule selection by means of a global
evolutionary optimization. We
conducted a complete empirical
study to test the performance of
our approach in terms of accuracy,
complexity, and runtime. The
results obtained were compared
and contrasted with four state-ofthe-art fuzzy classifiers for Big
Data (FBDT, FMDT, Chi-SparkRS, and CHI-BD). According to
this study, CFM-BD is able to provide competitive discrimination
capabilities using significantly simpler models composed of a few
rules of less than three antecedents,
employing five linguistic labels for
all variables."

A Novel Classification Method From the
Perspective of Fuzzy Social Networks
Based on Physical and Implicit Style
Features of Data, by S. Gu, Y. Nojima,
H. Ishibuchi, and S. Wang, IEEE
Transactions on Fuzzy Systems, Vol. 28,
No. 2, February 2020, pp. 361-375.
Digital Object Identifier: 10.1109/
TFUZZ.2019.2906855
"Many practical scenarios have
demanded that we should classify
unlabeled data more accurately
based on both physical features
(e.g., color, distance, or similarity)
and implicit style features of data.
As most extant classification algorithms classify unlabeled data based
only on their physical features, they
become weak in achieving expected classification results for many
scenarios. To work around this
drawback in this paper, a novel
classification method (FuCM)
from the perspective of fuzzy social
network based on both physical
and implicit style features of data is
proposed. Based on the proposed
fuzzy social network and its
dynamics about fuzzy influences of
nodes, FuCM comprises two stages. In its training stage, after the
fuzzy social network has been
built, it learns the topological
structure, reflecting physical features and implicit style features of
data by carrying out fuzzy influence dynamics in the built network. In its prediction stage, both
physical and implicit style features
of data are effectively integrated to
yield the double structure efficiency characterized by fuzzy influences of nodes. FuCM classifies
unlabeled data according to the
strongest connection measure
based on the proposed double
structure efficiency. FuCM does
not assume that both data distribution and the classification by physical features or by both physical and
implicit style features of data must
be known in advance. Thus, it is a
novel unified classification framework in this sense. In contrast to all

the nine comparative methods,
FuCM experimentally demonstrates its comparable classification
performance on most synthetic,
UCI and KEEL datasets, which
can be well classified based only on
physical features of data. Furthermore, it displays distinctive superiority on five case studies where
satisfactory classification certainly
depends on both physical and implicit style features."
IEEE Transactions on Evolutionary
Computation

An Experimental Method to Estimate
Running Time of Evolutionary Algorithms for Continuous Optimization, by
H. Huang, J. Su, Y. Zhang, and Z.
Hao, IEEE Transactions on Evolutionary
Computation, Vol. 24, No. 2, April
2020, pp. 275-289.
Digital Object Identifier: 10.1109/
TEVC.2019.2921547
"Running time analysis is a fundamental problem of critical importance in evolutionary computation.
However, the analysis results have
rarely been applied to advanced
evolutionary algorithms (EAs) in
practice, let alone their variants for
continuous optimization. In this
paper, an experimental method is
proposed for analyzing the running
time of EAs that are widely used for
solving continuous optimization
problems. Based on Glivenko-Cantelli theorem, the proposed method
simulates the distribution of gain,
which is introduced by average gain
model to characterize progress during the optimization process. Data
fitting techniques are subsequently
adopted to obtain a desired function for further analyses. To verify
the validity of the proposed method, experiments were conducted to
estimate the upper bounds on
expected first hitting time of various evolutionary strategies, such as
evolution strategy, standard evolution strategy, covariance matrix
adaptation evolution strategy, and its

AUGUST 2020 | IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE

IEEE Computational Intelligence Magazine - August 2020

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