Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/4388
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dc.contributor.authorVítor Manuel Cerqueiraen
dc.contributor.authorFábio Hernâni Pintoen
dc.contributor.authorCláudio Rebelo Sáen
dc.contributor.authorCarlos Manuel Soaresen
dc.date.accessioned2017-12-20T11:33:03Z-
dc.date.available2017-12-20T11:33:03Z-
dc.date.issued2016en
dc.identifier.urihttp://repositorio.inesctec.pt/handle/123456789/4388-
dc.identifier.urihttp://dx.doi.org/10.1007/978-3-319-46349-0_35en
dc.description.abstractWe describe a data mining workflow for predictive maintenance of the Air Pressure System in heavy trucks. Our approach is composed by four steps: (i) a filter that excludes a subset of features and examples based on the number of missing values (ii) a metafeatures engineering procedure used to create a meta-level features set with the goal of increasing the information on the original data; (iii) a biased sampling method to deal with the class imbalance problem; and (iv) boosted trees to learn the target concept. Results show that the metafeatures engineering and the biased sampling method are critical for improving the performance of the classifier.en
dc.languageengen
dc.relation6211en
dc.relation5832en
dc.relation5001en
dc.relation5527en
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.titleCombining Boosted Trees with Metafeature Engineering for Predictive Maintenanceen
dc.typeconferenceObjecten
dc.typePublicationen
Appears in Collections:CESE - Articles in International Conferences
LIAAD - Articles in International Conferences

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