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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/21/7007 |
| _version_ | 1797511809299644416 |
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| author | Erik Molino-Minero-Re Antonio A. Aguileta Ramon F. Brena Enrique Garcia-Ceja |
| author_facet | Erik Molino-Minero-Re Antonio A. Aguileta Ramon F. Brena Enrique Garcia-Ceja |
| author_sort | Erik Molino-Minero-Re |
| collection | DOAJ |
| 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 <i>k</i> 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 <i>T</i> 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. |
| first_indexed | 2024-03-10T05:53:16Z |
| format | Article |
| id | doaj.art-cddfed978c2a4404aaeb353f60d9926c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:53:16Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-cddfed978c2a4404aaeb353f60d9926c2023-11-22T21:35:16ZengMDPI AGSensors1424-82202021-10-012121700710.3390/s21217007Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple DomainsErik Molino-Minero-Re0Antonio A. Aguileta1Ramon F. Brena2Enrique Garcia-Ceja3Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas—Unidad Yucatán, Universidad Nacional Autónoma de México, Sierra Papacal, Yucatán 97302, MexicoFacultad de Matemáticas, Universidad Autónoma de Yucatán, Anillo Periférico Norte, Tablaje Cat. 13615, Colonia Chuburná Hidalgo Inn, Mérida, Yucatán 97110, MexicoTecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, MexicoOptimeering AS Tordenskioldsgate 6, 0160 Oslo, NorwayMulti-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 <i>k</i> 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 <i>T</i> 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.https://www.mdpi.com/1424-8220/21/21/7007sensor fusionclassificationSFFSmetadatastatistical signature |
| spellingShingle | Erik Molino-Minero-Re Antonio A. Aguileta Ramon F. Brena Enrique Garcia-Ceja Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains Sensors sensor fusion classification SFFS metadata statistical signature |
| 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 | sensor fusion classification SFFS metadata statistical signature |
| url | https://www.mdpi.com/1424-8220/21/21/7007 |
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