IEEE Computational Intelligence Magazine - February 2021 - 18

Publication
Spotlight

Haibo He, University of Rhode Island, USA
Jon Garibaldi, University of Nottingham, UK
Carlos A. Coello Coello, CINVESTAV-IPN, MEXICO
Julian Togelius, New York University, USA
Yaochu Jin, University of Surrey, UK
Yew Soon Ong, Nanyang Technological University, SINGAPORE
Hussein Abbass, University of New South Wales, AUSTRALIA

CIS Publication Spotlight

IEEE Transactions on Neural
Networks and Learning Systems

Digital Object Identifier: 10.1109/
TNNLS.2019.2945133
" The aim of multi-output learning is
to simultaneously predict multiple outputs given an input. It is an important
learning problem for decision-making
since making decisions in the real world
often involves multiple complex factors
and criteria. In recent times, an increasing number of research studies have
focused on ways to predict multiple
outputs at once. Such efforts have transpired in different forms according to the
particular multi-output learning problem under study. Classic cases of multioutput learning include multi-label
learning, multi-dimensional learning,
multi-target regression, and others. From
our survey of the topic, we were struck
by a lack in studies that generalize the
different forms of multi-output learning
into a common framework. This article
fills that gap with a comprehensive
review and analysis of the multi-output
learning paradigm. In particular, we
characterize the four Vs of multi-output
learning, i.e., volume, velocity, variety,
and veracity, and the ways in which the
Digital Object Identifier 10.1109/MCI.2020.3039065
Date of current version: 12 January 2021

©MASTERSERIES

Survey on Multi-Output Learning, by
D. Xu, Y. Shi, I. W. Tsang, Y. Ong, C.
Gong, and X. Shen, IEEE Transactions
on Neural Networks and Learning Systems, Vol. 31, No. 7, July 2020, pp.
2409-2429.

four Vs both benefit and bring challenges to multi-output learning by taking
inspiration from big data. We analyze the
life cycle of output labeling, present the
main mathematical definitions of multioutput learning, and examine the field's
key challenges and corresponding solutions as found in the literature. Several
model evaluation metrics and popular
data repositories are also discussed. Last
but not least, we highlight some emerging challenges with multi-output learning from the perspective of the four Vs
as potential research directions worthy
of further studies. "
Extracting Relational Explanations From
Deep Neural Networks: A Survey From
a Neural-Symbolic Perspective, by J.
Townsend, T. Chaton, and J. M.
Monteiro, IEEE Transactions on Neural Networks and Learning Systems, Vol.
31, No. 9, September 2020, pp.
3456-3470.
Digital Object Identifier: 10.1109/
TNNLS.2019.2944672

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | FEBRUARY 2021

" The term " explainable AI " refers to
the goal of producing artificially intelligent agents that are capable of providing
explanations for their decisions. Some
models (e.g., rule-based systems) are de--
signed to be explainable, while others are
less explicit " black boxes " for which their
reasoning remains a mystery. One example of the latter is the neural network, and
over the past few decades, researchers in
the field of neural-symbolic integration
(NSI) have sought to extract relational
knowledge from such networks. Extraction from deep neural networks, however,
has remained a challenge until recent
years in which many methods of extracting distinct, salient features from input or
hidden feature spaces of deep neural networks have been proposed. Furthermore,
methods of identifying relationships
between these features have also emerged.
This article presents examples of old and
new developments in extracting relational
explanations in order to argue that the latter have analogies in the former and, as
such, can be described in terms of longestablished taxonomies and frameworks
presented in early neural-symbolic literature. We also outline potential future
research directions that come to light
from this refreshed perspective. "
IEEE Transactions on
Fuzzy Systems

Probabilistic Forecasting With Fuzzy
Time Series, by P. C. L. Silva, H. J.
Sadaei, R. Ballini, and F. G. Guimarães, IEEE Transactions on Fuzzy
Systems, Vol. 28, No. 8, August 2020,
pp. 1771-1784.

IEEE Computational Intelligence Magazine - February 2021

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