Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance
Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance
dc.contributor.author | Vítor Manuel Cerqueira | en |
dc.contributor.author | Fábio Hernâni Pinto | en |
dc.contributor.author | Cláudio Rebelo Sá | en |
dc.contributor.author | Carlos Manuel Soares | en |
dc.date.accessioned | 2017-12-20T11:33:03Z | |
dc.date.available | 2017-12-20T11:33:03Z | |
dc.date.issued | 2016 | en |
dc.description.abstract | We 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.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/4388 | |
dc.identifier.uri | http://dx.doi.org/10.1007/978-3-319-46349-0_35 | en |
dc.language | eng | en |
dc.relation | 6211 | en |
dc.relation | 5832 | en |
dc.relation | 5001 | en |
dc.relation | 5527 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Combining Boosted Trees with Metafeature Engineering for Predictive Maintenance | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
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