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Fuzzy k-NN applied to mould detection

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The possibility to detect Aspergillus versicolor growing on different building materials by a metal oxide sensor array is studied. Results show that an accurate classification rate of 89 ± 3% can be obtained combining an extended linear discriminant analysis plus a fuzzy k-NN classifier. The classification ability of the classifier is assessed within the dataset by crossvalidation and also in a second dataset collected 5 months later. There is a slight decrease in the classification performance for all the algorithms, being the most sensitive the most accurate one.

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KUSKE, M., et al. Fuzzy k-NN applied to mould detection. Sensors and Actuators B-Chemical. 2005. Vol. 106, num. 52-60. ISSN 0925-4005. [consulted: 28 of June of 2026]. Available at: https://hdl.handle.net/2445/125468

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