IEEE Computational Intelligence Magazine - August 2021 - 94

IEEE Trans. Evol. Comput., vol. 25, no. 1, pp. 87-101,
2021. doi: 10.1109/TEVC.2020.3002229.
[38] M. W. Aslam, Z. Zhu, and A. K. Nandi, " Automatic
modulation classification using combination of genetic
programming and KNN, " IEEE Trans. Wirel. Commun.,
vol. 11, no. 8, pp. 2742-2750, 2012. doi: 10.1109/
TWC.2012.060412.110460.
[39] Z. Gao, C. Cecati, and S. X. Ding, " A survey of fault
diagnosis and fault-tolerant techniques-Part I: Fault diagnosis
with model-based and signal-based approaches, "
IEEE Trans. Ind. Electron., vol. 62, no. 6, pp. 3757-3767,
2015. doi: 10.1109/TIE.2015.2417501.
[40] J. R. Koza, Genetic Programming: On the Programming
of Computers by Means of Natural Selection. Cambridge,
MA: MIT Press, 1992.
[41] F. E. Otero, M. M. Silva, A. A. Freitas, and J. C.
Nievola, " Genetic programming for attribute construction
in data mining, " in Proc. EuroGP. Berlin: SpringerVerlag,
2003, pp. 384-393.
[42] M. Muharram and G. D. Smith, " Evolutionary
constructive induction, " IEEE Trans. Knowl. Data Eng.,
vol. 17, no. 11, pp. 1518-1528, 2005. doi: 10.1109/
TKDE.2005.182.
[43] H. Guo and A. K. Nandi, " Breast cancer diagnosis
using genetic programming generated feature, " Pattern
Recogn., vol. 39, no. 5, pp. 980-987, 2006. doi: 10.1016/j.
patcog.2005.10.001.
[44] H. Guo, Q. Zhang, and A. K. Nandi, " Feature extraction
and dimensionality reduction by genetic programming
based on the fisher criterion, " Expert Syst.,
vol. 25, no. 5, pp. 444-459, 2008. doi: 10.1111/j.1468
-0394.2008.00451.x.
[45] K. Neshatian, M. Zhang, and P. Andreae, " A filter
approach to multiple feature construction for symbolic
learning classifiers using genetic programming, " IEEE
Trans. Evol. Comput., vol. 16, no. 5, pp. 645-661, 2012.
doi: 10.1109/TEVC.2011.2166158.
[46] B. Tran, B. Xue, and M. Zhang, " Genetic programming
for multiple-feature construction on highdimensional
classification, " Pattern Recogn., vol. 93, pp.
404-417, 2019. doi: 10.1016/j.patcog.2019.05.006.
[47] J. Ma and G. Teng, " A hybrid multiple feature construction
approach for classification using genetic programming, "
Appl. Soft Comput., vol. 80, pp. 687-699,
2019. doi: 10.1016/j.asoc.2019.04.039.
[48] L. Zhang, L. B. Jack, and A. K. Nandi, " Fault detection
using genetic programming, " Mech. Syst. Signal
Process., vol. 19, no. 2, pp. 271-289, 2005. doi: 10.1016/j.
ymssp.2004.03.002.
[49] H. Guo, L. B. Jack, and A. K. Nandi, " Feature generation
using genetic programming with application to
fault classification, " IEEE Trans. Syst., Man, Cybern. C,
Cybern., vol. 35, no. 1, pp. 89-99, 2005. doi: 10.1109/
TSMCB.2004.841426.
[50] J. Xuan, H. Jiang, T. Shi, and G. Liao, " Gear fault
classification using genetic programming and support vector
machines, " Int. J. Inf. Technol., vol. 11, no. 9, pp. 18-27,
2005.
[51] D. J. Montana, " Strongly typed genetic programming, "
Evol. Comput., vol. 3, no. 2, pp. 199-230, 1995.
doi: 10.1162/evco.1995.3.2.199.
[52] Y. Lei, B. Yang, X. Jiang, F. Jia, N. Li, and A. K.
Nandi, " Applications of machine learning to machine
fault diagnosis: A review and roadmap, " Mech. Syst. Signal
Process., vol. 138, 2020.
[53] X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma, " A survey
on ensemble learning, " Front. Comput. Sci., pp. 1-18, 2020.
[54] " Case western reserve university bearing data center. "
Accessed: Apr. 2018. [Online]. http://csegroups
.case.edu/bearingdatacenter/home/
[55] B. Wang, Y. Lei, N. Li, and N. Li, " A hybrid prognostics
approach for estimating remaining useful life of
rolling element bearings, " IEEE Trans. Reliab., vol. 69, no.
1, pp. 401-412, 2020. doi: 10.1109/TR.2018.2882682.
[56] Y. Bi, B. Xue, and M. Zhang, " An effective feature
learning approach using genetic programming with image
descriptors for image classification [research frontier], "
IEEE Comput. Intell. Mag., vol. 15, no. 2, pp. 65-
77, 2020. doi: 10.1109/MCI.2020.2976186.
[57] F.-A. Fortin et al., " DEAP: Evolutionary algorithms
made easy, " J. Mach. Learn. Res., vol. 13, pp. 2171-2175, 2012.
[58] F. Pedregosa et al., " Scikit-learn: Machine learning in
Python, " J. Mach. Learn. Res., vol. 12, pp. 2825-2830, 2011.
[59] L. v. d. Maaten and G. Hinton, " Visualizing data using TSNE, "
J. Mach. Learn. Res., vol. 9, pp. 2579-2605, Nov. 2008.
Publication Spotlight (continued from page 7)
by O. A. Ibrahim, J. M. Keller, and J. C.
Bezdek, IEEE Transactions on Emerging
Topics in Computational Intelligence, Vol.
5, No. 2, April 2021, pp. 262-273.
Digital Object
Identifier: 10.1109/
TETCI.2019.2909521
" Dunn's internal cluster validity
index is used to assess partition quality
and identify a " best " crisp c-partition of
n objects built from static data sets. This
index is quite sensitive to inliers and
outliers in the input data, so a subsequent
study developed a family of 17
generalized Dunn's indices that extend
and improve the original measure in
various ways. This paper presents online
versions of two modified generalized
Dunn's indices that can be used for the
dynamic evaluation of an evolving (cluster)
structure in streaming data. We
argue that this method is a good way to
monitor the ongoing performance of
streaming clustering algorithms, and we
illustrate several types of inferences that
can be drawn from such indices. Streaming
clustering algorithms are incremental,
process incoming data points only
once and then discard them, adapt as the
data stream evolves, flag outliers, and
most importantly, spawn new emerging
structures. We compare the two new
indices to the incremental Xie-Beni and
Davies-Boudin indices, which to our
knowledge offer the only comparable
approach, with numerical examples on a
variety of synthetic and real datasets. "
IEEE Transactions on Artificial
Intelligence
Procedural Memory Augmented Deep
Reinforcement Learning, by Y. Ma, J.
Brooks, H. Li, and J. C. Principe, IEEE
Transactions on Artificial Intelligence, Vol.
1, No. 2, Oct 2020, pp. 105-120.
Digital Object Identifier: 10.1109/
TAI.2021.3054722
" Inspired by the human brain, we propose
an external memory-augmented
decision-making architecture for video
processing. A self-organizing object detector
is employed as a frontend to deconstruct
the environment. This is done by
extracting events from the flow of time
and detecting objects within the frames.
By employing an extra working memory
94 IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2021
where objects are temporarily stored, the
system can extract properties of the stored
objects related to the task. We propose a
deep reinforcement learning (RL) neural
network to learn affordances, i.e., a
sequence of actions to manipulate these
objects. The RL network and object
detector are trained alternatively. After
both the network and detector are
trained, the objects and their affordances
are transferred to an external memory.
They are then utilized when the same
objects are detected in input frames. Here,
we use a combination of a dictionary and
a linked list for the external memory that
can be accessed by either content or temporal
order. This dual access is motivated
by the temporal property of human procedural
memory. The proposed memoryaugmented
RL framework brings
advantages of transferability, explainability
and computational efficiency with respect
to conventional deep learning architectures.
We validate the framework on the
video game Super Mario Brothers to
show superiority to some classical deep
RL architectures and exemplify these
three advantages. "
http://csegroups.case.edu/bearingdatacenter/home/ http://csegroups.case.edu/bearingdatacenter/home/
IEEE Computational Intelligence Magazine - August 2021

Table of Contents for the Digital Edition of IEEE Computational Intelligence Magazine - August 2021
