Computational Intelligence - February 2017 - 13

Publication
Spotlight

Haibo He, Jon Garibaldi,
Kay Chen Tan, Graham Kendall,
and Yaochu Jin

CIS Publication Spotlight

Why Deep Learning Works: A Manifold
Disentanglement Perspective, by P. P.
Brahma, D. Wu, and Y. She, IEEE
Transactions on Neural Networks and
Learning Systems, Vol. 27, No. 10,
October 2016, pp. 1997-2008.
Digital Object Identifier: 10.1109/
TNNLS.2015.2496947
"Deep hierarchical representations of
the data have been found out to provide
better informative features for several
machine learning applications. In addition, multilayer neural networks surprisingly tend to achieve better performance
when they are subject to an unsupervised
pretraining. The booming of deep learning motivates researchers to identify the
factors that contribute to
its success. One possible
reason identified is the
flattening of manifoldshaped data in higher layers of neural networks.
However, it is not clear
how to measure the flattening of such manifoldshaped data and what
amount of flattening a
deep neural network can
achieve. For the first time, this paper provides quantitative evidence to validate the
flattening hypothesis. To achieve this, we

Digital Object Identifier 10.1109/MCI.2016.2627667
Date of publication: 6 January 2017

propose a few quantities for measuring
manifold entanglement under certain
assumptions and conduct experiments
with both synthetic and real-world data.
Our experimental results validate the
proposition and lead to new insights on
deep learning."
Oversampling the Minority Class in the
Feature Space, by M. Pérez-Ortiz, P. A.
Gutiérrez, P. Tino, and C. HervásMartínez, IEEE Transactions on Neural
Networks and Learning Systems, Vol.
27, No. 9, September 2016, pp.
1947-1961.
Digital Object Identifier: 10.1109/
TNNLS.2015.2461436
"The imbalanced nature of some
real-world data is one of the current
challenges for machine lear ning
researchers. One common approach oversamples the minority class
through convex combination of its patterns.
We explore the general
idea of synthetic oversampling in the feature
space induced by a kernel function (as opposed
to input space). If the
kernel function matches
the underlying problem, the classes will
be linearly separable and synthetically
generated patterns will lie on the
minority class region. Since the feature
space is not directly accessible, we use
the empirical feature space (EFS) (a
Euclidean space isomorphic to the fea©masterseries

IEEE Transactions on Neural
Networks and Learning Systems

ture space) for oversampling purposes.
The proposed method is framed in the
context of support vector machines,
where the imbalanced data sets can
pose a serious hindrance. The idea is
investigated in three scenarios: 1) oversampling in the full and reduced-rank
EFSs; 2) a kernel learning technique
maximizing the data class separation to
study the influence of the feature space
structure (implicitly defined by the
kernel function); and 3) a unified
framework for preferential oversampling that spans some of the previous
approaches in the literature. We support
our investigation with extensive experiments over 50 imbalanced data sets."
IEEE Transactions
on Fuzzy Systems

On Pythagorean and Complex Fuzzy
Set Operations, by S. Dick, R. R.
Yager, and O. Yazdanbakhsh, IEEE
Transactions on Fuzzy Systems, Vol. 24,
No. 5, October 2016, pp. 1009-1021.
Digital Object Identifier: 10.1109/
TFUZZ.2015.2500273
"Complex fuzzy logic is a new multivalued logic system that has emerged
in the last decade. At this time, there are
a limited number of known instances of
complex fuzzy logic, and only a partial
exploration of their properties. There has
also been relatively little progress in
developing interpretations of complexvalued membership grades. In this paper,
the authors address both problems by
examining the recently developed

FEBRUARY 2017 | IEEE CompUtAtIonAl IntEllIgEnCE mAgAzInE

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Table of Contents for the Digital Edition of Computational Intelligence - February 2017

Computational Intelligence - February 2017 - Cover1
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