The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network

Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In thi...

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Main Author: Khalil Moshkbar-Bakhshayesh
Format: Article
Language:English
Published: Elsevier 2021-12-01
Series:Nuclear Engineering and Technology
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S1738573321003776
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spelling doaj-01af7566c3824d95b1f817dba3de7ddf2021-09-17T04:34:32ZengElsevierNuclear Engineering and Technology1738-57332021-12-01531239443951The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural networkKhalil Moshkbar-Bakhshayesh0Department of Energy Engineering, Sharif University of Technology, Azadi Ave., Tehran, IranSeveral reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.http://www.sciencedirect.com/science/article/pii/S1738573321003776Combination techniqueFeatures selection techniqueHeterogeneous ensembleParameter estimationSupervised learning algorithm
collection DOAJ
language English
format Article
sources DOAJ
author Khalil Moshkbar-Bakhshayesh
spellingShingle Khalil Moshkbar-Bakhshayesh
The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network
Nuclear Engineering and Technology
Combination technique
Features selection technique
Heterogeneous ensemble
Parameter estimation
Supervised learning algorithm
author_facet Khalil Moshkbar-Bakhshayesh
author_sort Khalil Moshkbar-Bakhshayesh
title The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network
title_short The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network
title_full The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network
title_fullStr The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network
title_full_unstemmed The ensemble approach in comparison with the diverse feature selection techniques for estimating NPPs parameters using the different learning algorithms of the feed-forward neural network
title_sort ensemble approach in comparison with the diverse feature selection techniques for estimating npps parameters using the different learning algorithms of the feed-forward neural network
publisher Elsevier
series Nuclear Engineering and Technology
issn 1738-5733
publishDate 2021-12-01
description Several reasons such as no free lunch theorem indicate that there is not a universal Feature selection (FS) technique that outperforms other ones. Moreover, some approaches such as using synthetic dataset, in presence of large number of FS techniques, are very tedious and time consuming task. In this study to tackle the issue of dependency of estimation accuracy on the selected FS technique, a methodology based on the heterogeneous ensemble is proposed. The performance of the major learning algorithms of neural network (i.e. the FFNN-BR, the FFNN-LM) in combination with the diverse FS techniques (i.e. the NCA, the F-test, the Kendall's tau, the Pearson, the Spearman, and the Relief) and different combination techniques of the heterogeneous ensemble (i.e. the Min, the Median, the Arithmetic mean, and the Geometric mean) are considered. The target parameters/transients of Bushehr nuclear power plant (BNPP) are examined as the case study. The results show that the Min combination technique gives the more accurate estimation. Therefore, if the number of FS techniques is m and the number of learning algorithms is n, by the heterogeneous ensemble, the search space for acceptable estimation of the target parameters may be reduced from n × m to n × 1. The proposed methodology gives a simple and practical approach for more reliable and more accurate estimation of the target parameters compared to the methods such as the use of synthetic dataset or trial and error methods.
topic Combination technique
Features selection technique
Heterogeneous ensemble
Parameter estimation
Supervised learning algorithm
url http://www.sciencedirect.com/science/article/pii/S1738573321003776
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