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

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Oliveira, M (Author) Other; Torgo, L (Author) FCUP; Costa, VS (Author) FCUP

Conference proceedings International

Title: DISCOVERY SCIENCE, (DS 2016)

Pages: 183-197

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

Bari, ITALY, OCT 19-21, 2016

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 3 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15