Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches
Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches
dc.contributor.author | Mariana Rafaela Oliveira | en |
dc.contributor.author | Luís Torgo | en |
dc.contributor.author | Vítor Santos Costa | en |
dc.date.accessioned | 2017-12-12T13:27:50Z | |
dc.date.available | 2017-12-12T13:27:50Z | |
dc.date.issued | 2016 | en |
dc.description.abstract | We present and evaluate two different methods for building spatio-temporal features: a propositional method and a method based on propositionalisation of relational clauses. Our motivating application, a regression problem, requires the prediction of the fraction of each Portuguese parish burnt yearly by wildfires - a problem with a strong socio-economic and environmental impact in the country. We evaluate and compare how these methods perform individually and combined together. We successfully use under-sampling to deal with the high skew in the data set. We find that combining the approaches significantly improves the similar results obtained by each method individually. | en |
dc.identifier.uri | http://repositorio.inesctec.pt/handle/123456789/3909 | |
dc.identifier.uri | http://dx.doi.org/10.1007/978-3-319-46307-0_12 | en |
dc.language | eng | en |
dc.relation | 6110 | en |
dc.relation | 4982 | en |
dc.relation | 5129 | en |
dc.rights | info:eu-repo/semantics/openAccess | en |
dc.title | Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches | en |
dc.type | conferenceObject | en |
dc.type | Publication | en |
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