RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY
2007; Wiley; Volume: 88; Issue: 11 Linguagem: Inglês
10.1890/07-0539.1
ISSN1939-9170
AutoresD. Richard Cutler, Thomas C. Edwards, Karen H. Beard, Adele Cutler, Kyle T. Hess, Jacob Gibson, Joshua J. Lawler,
Tópico(s)Rangeland and Wildlife Management
ResumoEcologyVolume 88, Issue 11 p. 2783-2792 Article RANDOM FORESTS FOR CLASSIFICATION IN ECOLOGY D. Richard Cutler, Corresponding Author D. Richard Cutler Richard.Cutler@usu.edu Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USA E-mail: Richard.Cutler@usu.eduSearch for more papers by this authorThomas C. Edwards Jr., Thomas C. Edwards Jr. Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USA U.S. Geological Survey, Utah Cooperative Fish and Wildlife Research Unit, Utah State University, Logan, Utah 84322-5290 USASearch for more papers by this authorKaren H. Beard, Karen H. Beard Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah 84322-5230 USASearch for more papers by this authorAdele Cutler, Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USASearch for more papers by this authorKyle T. Hess, Kyle T. Hess Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USASearch for more papers by this authorJacob Gibson, Jacob Gibson Department of Wildland Resources, Utah State University, Logan, Utah 84322-5230 USASearch for more papers by this authorJoshua J. Lawler, Joshua J. Lawler College of Forest Resources, University of Washington, Seattle, Washington 98195-2100 USASearch for more papers by this author D. Richard Cutler, Corresponding Author D. Richard Cutler Richard.Cutler@usu.edu Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USA E-mail: Richard.Cutler@usu.eduSearch for more papers by this authorThomas C. Edwards Jr., Thomas C. Edwards Jr. Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USA U.S. Geological Survey, Utah Cooperative Fish and Wildlife Research Unit, Utah State University, Logan, Utah 84322-5290 USASearch for more papers by this authorKaren H. Beard, Karen H. Beard Department of Wildland Resources and Ecology Center, Utah State University, Logan, Utah 84322-5230 USASearch for more papers by this authorAdele Cutler, Adele Cutler Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USASearch for more papers by this authorKyle T. Hess, Kyle T. Hess Department of Mathematics and Statistics, Utah State University, Logan, Utah 84322-3900 USASearch for more papers by this authorJacob Gibson, Jacob Gibson Department of Wildland Resources, Utah State University, Logan, Utah 84322-5230 USASearch for more papers by this authorJoshua J. Lawler, Joshua J. Lawler College of Forest Resources, University of Washington, Seattle, Washington 98195-2100 USASearch for more papers by this author First published: 01 November 2007 https://doi.org/10.1890/07-0539.1Citations: 2,638 Corresponding Editor: A. M. Ellison. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract Classification procedures are some of the most widely used statistical methods in ecology. Random forests (RF) is a new and powerful statistical classifier that is well established in other disciplines but is relatively unknown in ecology. Advantages of RF compared to other statistical classifiers include (1) very high classification accuracy; (2) a novel method of determining variable importance; (3) ability to model complex interactions among predictor variables; (4) flexibility to perform several types of statistical data analysis, including regression, classification, survival analysis, and unsupervised learning; and (5) an algorithm for imputing missing values. We compared the accuracies of RF and four other commonly used statistical classifiers using data on invasive plant species presence in Lava Beds National Monument, California, USA, rare lichen species presence in the Pacific Northwest, USA, and nest sites for cavity nesting birds in the Uinta Mountains, Utah, USA. We observed high classification accuracy in all applications as measured by cross-validation and, in the case of the lichen data, by independent test data, when comparing RF to other common classification methods. We also observed that the variables that RF identified as most important for classifying invasive plant species coincided with expectations based on the literature. Citing Literature Supporting Information Filename Description https://dx.doi.org/10.6084/m9.figshare.c.3300029 Research data pertaining to this article is located at figshare.com: Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article. Volume88, Issue11November 2007Pages 2783-2792 RelatedInformation
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