International Journal of Computational Intelligence Systems

Volume 12, Issue 2, 2019, Pages 1635 - 1648

A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization

Authors
Saman Akbarzadeh*, Selam Ahderom, Kamal Alameh
Electron Science Research Institute, Edith Cowan University, JO23.241, 270 Joondalup Drive, Joondalup, Western Australia 6027, Australia
*Corresponding author. Email: s.akbarzadeh@ecu.edu.au
Corresponding Author
Saman Akbarzadeh
Received 27 May 2019, Accepted 16 December 2019, Available Online 23 December 2019.
DOI
10.2991/ijcis.d.191219.001How to use a DOI?
Keywords
Optimization; Convolutional neural network; Hyperparameter; Design of experiment; Taguchi; Deep learning
Abstract

Algorithms based on convolutional neural networks (CNNs) have been great attention in image processing due to their ability to find patterns and recognize objects in a wide range of scientific and industrial applications. Finding the best network and optimizing its hyperparameters for a specific application are central challenges for CNNs. Most state-of-the-art CNNs are manually designed, while techniques for automatically finding the best architecture and hyperparameters are computationally intensive, and hence, there is a need to severely limit their search space. This paper proposes a fast statistical method for CNN parameter optimization, which can be applied in many CNN applications and provides more explainable results. The authors specifically applied Taguchi based experimental designs for network optimization in a basic network, a simplified Inception network and a simplified Resnet network, and conducted a comparison analysis to assess their respective performance and then to select the hyperparameters and networks that facilitate faster training and provide better accuracy. The results show that up to a 6% increase in classification accuracy can be achieved after parameter optimization.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1635 - 1648
Publication Date
2019/12/23
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191219.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Saman Akbarzadeh
AU  - Selam Ahderom
AU  - Kamal Alameh
PY  - 2019
DA  - 2019/12/23
TI  - A Statistical Approach to Provide Explainable Convolutional Neural Network Parameter Optimization
JO  - International Journal of Computational Intelligence Systems
SP  - 1635
EP  - 1648
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191219.001
DO  - 10.2991/ijcis.d.191219.001
ID  - Akbarzadeh2019
ER  -