Predicting distribution of a new forest disease using one-class SVMs

Authors:
Qinghua Guo;Maggi Kelly;Catherine Graham
Affiliations:
-;-;-
Venue:
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Year:
2003

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Abstract

In California, a newly discovered virulent pathogen(Phytophthora ramorum) has killed thousands of nativeoak trees. Mapping the potential distribution of thepathogen is essential for decision makers to assess therisk of the pathogen and aid in preventing its furtherspread. Most methods used to map potential ranges ofspecies (e.g. multivariate or logistic regression) requireboth presence and absence data, the latter of which is notalways feasibly collected. In this study, we present theone-class Support Vector Machine (SVM) to predict thepotential distribution of Sudden Oak Death in California.The model was developed using presence data collectedthroughout the state, and tested for accuracy using a 5-fold cross-validation approach. The model performedwell, and provided 91% predicted accuracy. We believeone-class SVM when coupled with GeographicalInformation Systems (GIS) will become a very usefulmethod to deal with presence-only data in ecologicalanalysis over a range of scales.