{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T00:08:09Z","timestamp":1763078889433,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PAPIIT-DGAPA (UNAM)","award":["103420"],"award-info":[{"award-number":["103420"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multi-sensor fusion intends to boost the general reliability of a decision-making procedure or allow one sensor to compensate for others\u2019 shortcomings. This field has been so prominent that authors have proposed many different fusion approaches, or \u201carchitectures\u201d 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/s21217007","type":"journal-article","created":{"date-parts":[[2021,10,24]],"date-time":"2021-10-24T22:07:11Z","timestamp":1635113231000},"page":"7007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Improved Accuracy in Predicting the Best Sensor Fusion Architecture for Multiple Domains"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7615-4431","authenticated-orcid":false,"given":"Erik","family":"Molino-Minero-Re","sequence":"first","affiliation":[{"name":"Instituto de Investigaciones en Matem\u00e1ticas Aplicadas y en Sistemas\u2014Unidad Yucat\u00e1n, Universidad Nacional Aut\u00f3noma de M\u00e9xico, Sierra Papacal, Yucat\u00e1n 97302, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5155-3543","authenticated-orcid":false,"given":"Antonio A.","family":"Aguileta","sequence":"additional","affiliation":[{"name":"Facultad de Matem\u00e1ticas, Universidad Aut\u00f3noma de Yucat\u00e1n, Anillo Perif\u00e9rico Norte, Tablaje Cat. 13615, Colonia Chuburn\u00e1 Hidalgo Inn, M\u00e9rida, Yucat\u00e1n 97110, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0995-2273","authenticated-orcid":false,"given":"Ramon F.","family":"Brena","sequence":"additional","affiliation":[{"name":"Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501 Sur, Monterrey, NL 64849, Mexico"}]},{"given":"Enrique","family":"Garcia-Ceja","sequence":"additional","affiliation":[{"name":"Optimeering AS Tordenskioldsgate 6, 0160 Oslo, Norway"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.inffus.2016.09.005","article-title":"Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges","volume":"35","author":"Gravina","year":"2017","journal-title":"Inf. Fusion"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8961","DOI":"10.3390\/s140508961","article-title":"A ubiquitous and low-cost solution for movement monitoring and accident detection based on sensor fusion","volume":"14","author":"Felisberto","year":"2014","journal-title":"Sensors"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Huang, C.W., and Narayanan, S. (2016, January 21\u201323). 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