IEEE Computational Intelligence Magazine - November 2019 - 8

Publication
Spotlight

Haibo He, Jon Garibaldi, Kay Chen Tan,
Julian Togelius, Yaochu Jin, and
Yew Soon Ong

CIS Publication Spotlight

IEEE Transactions on Neural
Networks and Learning Systems

Denoising Adversarial Autoencoders, by
A. Creswell and A. A. Bharath, IEEE
Transactions on Neural Networks and
Learning Systems, Vol. 30, No. 4, April
2019, pp. 968-984.
Digital Object Identifier: 10.1109/
TNNLS.2018.2852738
"Unsupervised learning is of growing
interest because it unlocks the potential
held in vast amounts of unlabeled data to
learn useful representations for inference.
Autoencoders, a form of generative
model, may be trained by learning to
reconstruct unlabeled input data from a
latent representation space. More robust
representations may be produced by an
autoencoder if it learns to recover clean
input samples from corrupted ones. Representations may be further improved by
introducing regularization during training to shape the distribution of the
encoded data in the latent space. We suggest denoising adversarial autoencoders
(AAEs), which combine denoising and
regularization, shaping the distribution of
latent space using adversarial training. We
introduce a novel analysis that shows how
denoising may be incorporated into the
training and sampling of AAEs. Experiments are performed to assess the contributions that denoising makes to the
learning of representations for classification and sample synthesis. Our results
suggest that autoencoders trained using a
Digital Object Identifier 10.1109/MCI.2019.2937606
Date of current version: 14 October 2019

(IMAGE SOURCE: WOODCUT CLIP ART-©IMAGE CLUB)

denoising criterion achieve higher classification performance and can synthesize
samples that are more consistent with the
input data than those trained without a
corruption process."
Deep Neural Network Initialization
with Decision Trees, by K. D. Humbird,
J. L. Peterson, and R. G. Mcclarren,
IEEE Transactions on Neural Networks
and Learning Systems, Vol. 30, No. 5,
May 2019, pp. 1286-1295.
Digital Object Identifier: 10.1109/
TNNLS.2018.2869694
"In this paper, a novel, automated
process for constructing and initializing
deep feedforward neural networks based
on decision trees is presented. The proposed algorithm maps a collection of
decision trees trained on the data into a
collection of initialized neural networks
with the structures of the networks

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | NOVEMBER 2019

determined by the structures of the trees.
The tree-informed initialization acts as a
warm-start to the neural network training process, resulting in efficiently trained,
accurate networks. These models, referred
to as "deep jointly informed neural networks" (DJINN), demonstrate high predictive performance for a variety of
regression and classification data sets
and display comparable performance to
Bayesian hyperparameter optimization at
a lower computational cost. By combining the user-friendly features of decision
tree models with the flexibility and scalability of deep neural networks, DJINN
is an attractive algorithm for training predictive models on a wide range of complex data sets."
IEEE Transactions
on Fuzzy Systems

A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers
Based on Data Complexity, by J.
Cózar, A. Fernández, F. Herrera,
and J. A. Gámez, IEEE Transactions
on Fuzzy Systems, Vol. 27, No. 4,
April 2019, pp. 701-715.
Digital Object Identifier: 10.1109/
TFUZZ.2018.2866967
"There is a wide variety of studies
that propose different classifiers to solve a
large amount of problems in distinct classification scenarios. The no free lunch
theorem states that if we use a big enough
set of varied problems, all classifiers
would be equivalent in performance.
From another point of view, the performance of the classifiers is dependant of

IEEE Computational Intelligence Magazine - November 2019

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