{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T05:04:25Z","timestamp":1783573465844,"version":"3.55.0"},"reference-count":41,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T00:00:00Z","timestamp":1708560000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research as part of the \u201cResearch for Civil Security\u201d program","award":["13N15415"],"award-info":[{"award-number":["13N15415"]}]},{"name":"German Federal Ministry of Education and Research as part of the \u201cResearch for Civil Security\u201d program","award":["13N15420"],"award-info":[{"award-number":["13N15420"]}]},{"name":"German Federal Ministry of Education and Research as part of the \u201cResearch for Civil Security\u201d program","award":["13N15565"],"award-info":[{"award-number":["13N15565"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Effective early fire detection is crucial for preventing damage to people and buildings, especially in fire-prone historic structures. However, due to the infrequent occurrence of fire events throughout a building\u2019s lifespan, real-world data for training models are often sparse. In this study, we applied feature representation transfer and instance transfer in the context of early fire detection using multi-sensor nodes. The goal was to investigate whether training data from a small-scale setup (source domain) can be used to identify various incipient fire scenarios in their early stages within a full-scale test room (target domain). In a first step, we employed Linear Discriminant Analysis (LDA) to create a new feature space solely based on the source domain data and predicted four different fire types (smoldering wood, smoldering cotton, smoldering cable and candle fire) in the target domain with a classification rate up to 69% and a Cohen\u2019s Kappa of 0.58. Notably, lower classification performance was observed for sensor node positions close to the wall in the full-scale test room. In a second experiment, we applied the TrAdaBoost algorithm as a common instance transfer technique to adapt the model to the target domain, assuming that sparse information from the target domain is available. Boosting the data from 1% to 30% was utilized for individual sensor node positions in the target domain to adapt the model to the target domain. We found that additional boosting improved the classification performance (average classification rate of 73% and an average Cohen\u2019s Kappa of 0.63). However, it was noted that excessively boosting the data could lead to overfitting to a specific sensor node position in the target domain, resulting in a reduction in the overall classification performance.<\/jats:p>","DOI":"10.3390\/s24051428","type":"journal-article","created":{"date-parts":[[2024,2,22]],"date-time":"2024-02-22T11:28:47Z","timestamp":1708601327000},"page":"1428","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Classification in Early Fire Detection Using Multi-Sensor Nodes\u2014A Transfer Learning Approach"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3412-3693","authenticated-orcid":false,"given":"Pascal","family":"Vorwerk","sequence":"first","affiliation":[{"name":"Faculty of Process- and Systems Engineering, Institute of Apparatus and Environmental Technology, Otto von Guericke University of Magdeburg, Universit\u00e4tsplatz 2, 39106 Magdeburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J\u00f6rg","family":"Kelleter","sequence":"additional","affiliation":[{"name":"GTE Industrieelektronik GmbH, Helmholtzstr. 21, 38-40, 41747 Viersen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"GTE Industrieelektronik GmbH, Helmholtzstr. 21, 38-40, 41747 Viersen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6006-1284","authenticated-orcid":false,"given":"Ulrich","family":"Krause","sequence":"additional","affiliation":[{"name":"Faculty of Process- and Systems Engineering, Institute of Apparatus and Environmental Technology, Otto von Guericke University of Magdeburg, Universit\u00e4tsplatz 2, 39106 Magdeburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/BF01040755","article-title":"Using multivariate statistical methods to detect fires","volume":"32","author":"McAvoy","year":"1996","journal-title":"Fire Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.firesaf.2007.01.006","article-title":"Fire detection using smoke and gas sensors","volume":"42","author":"Chen","year":"2007","journal-title":"Fire Saf. 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