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.
CITATION STYLE
Oliveira, M., Torgo, L., & Costa, V. S. (2016). Predicting wildfires propositional and relational spatio-temporal pre-processing approaches. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9956 LNAI, pp. 183–197). Springer Verlag. https://doi.org/10.1007/978-3-319-46307-0_12
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