Predicting Wildfires Propositional and Relational Spatio-Temporal Pre-processing Approaches

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Oliveira, M (Autor) Outra; Torgo, L (Autor) FCUP; Costa, VS (Autor) FCUP

Ata de Conferência Internacional

Título: DISCOVERY SCIENCE, (DS 2016)

Páginas: 183-197

19th International Conference on Discovery Science (DS) / 27th International Conference on Algorithmic Learning Theory (ALT)

Bari, ITALY, OCT 19-21, 2016

Indexação

ISI Web of Knowledge - 4 Citações

Scopus - 3 Citações

Outras Informações

ID Authenticus: P-00M-3X9

DOI: 10.1007/978-3-319-46307-0_12

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 15