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Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains
Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are struc...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586946/ https://www.ncbi.nlm.nih.gov/pubmed/34770318 http://dx.doi.org/10.3390/s21217007 |
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author | Molino-Minero-Re, Erik Aguileta, Antonio A. Brena, Ramon F. Garcia-Ceja, Enrique |
author_facet | Molino-Minero-Re, Erik Aguileta, Antonio A. Brena, Ramon F. Garcia-Ceja, Enrique |
author_sort | Molino-Minero-Re, Erik |
collection | PubMed |
description | Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis’s first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains. |
format | Online Article Text |
id | pubmed-8586946 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85869462021-11-13 Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains Molino-Minero-Re, Erik Aguileta, Antonio A. Brena, Ramon F. Garcia-Ceja, Enrique Sensors (Basel) Article Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others’ shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or “architectures” as we call them when they are structurally different, so it is now challenging to prescribe which one is better for a specific collection of sensors and a particular application environment, other than by trial and error. We propose an approach capable of predicting the best fusion architecture (from predefined options) for a given dataset. This method involves the construction of a meta-dataset where statistical characteristics from the original dataset are extracted. One challenge is that each dataset has a different number of variables (columns). Previous work took the principal component analysis’s first k components to make the meta-dataset columns coherent and trained machine learning classifiers to predict the best fusion architecture. In this paper, we take a new route to build the meta-dataset. We use the Sequential Forward Floating Selection algorithm and a T transform to reduce the features and match them to a given number, respectively. Our findings indicate that our proposed method could improve the accuracy in predicting the best sensor fusion architecture for multiple domains. MDPI 2021-10-22 /pmc/articles/PMC8586946/ /pubmed/34770318 http://dx.doi.org/10.3390/s21217007 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Molino-Minero-Re, Erik Aguileta, Antonio A. Brena, Ramon F. Garcia-Ceja, Enrique Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains |
title | Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains |
title_full | Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains |
title_fullStr | Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains |
title_full_unstemmed | Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains |
title_short | Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains |
title_sort | improved accuracy in predicting the best sensor fusion architecture for multiple domains |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8586946/ https://www.ncbi.nlm.nih.gov/pubmed/34770318 http://dx.doi.org/10.3390/s21217007 |
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