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

Content Provider	IEEE Xplore Digital Library
Author	Guo, Q. Kelly, M. Graham, C.
Copyright Year	2003
Description	Author affiliation: Dept. of Environ. Sci., Pol. & Manage., California Univ., Berkeley, CA, USA (Guo, Q.; Kelly, M.)
Abstract	In California, a newly discovered virulent pathogen (Phytophthora ramorum) has killed thousands of native oak trees. Mapping the potential distribution of the pathogen is essential for decision makers to assess the risk of the pathogen and aid in preventing its further spread. Most methods used to map potential ranges of species (e.g. multivariate or logistic regression) require both presence and absence data, the latter of which is not always feasibly collected. We present the one-class support vector machine (SVM) to predict the potential distribution of sudden oak death in California. The model was developed using presence data collected throughout the state, and tested for accuracy using a 5-fold cross-validation approach. The model performed well, and provided 91% predicted accuracy. We believe one-class SVM when coupled with geographical information systems (GIS) become a very useful method to deal with presence-only data in ecological analysis over a range of scales.
Sponsorship	IEEE Comput. Soc. Tech. Committee on Computational Intelligence IEEE Comput. Soc. Tech. Committee on Pattern Analysis and Machine Intelligence
Starting Page	719
Ending Page	722
File Size	2444736
Page Count	4
File Format	PDF
ISBN	0769519784
DOI	10.1109/ICDM.2003.1251016
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-11-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diseases Pathogens Support vector machines Biological system modeling Logistics Testing Predictive models Accuracy Information systems Geographic Information Systems
Content Type	Text
Resource Type	Article

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