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期刊名称: Ecology
Volume:88    Issue:11        Page:2783-2792
ISSN:0012-9658

Random Forests for Classification in Ecology期刊论文

作者: D. Richard Cutler Thomas C. Edwards Karen H. Beard Adele Cutler Kyle T. Hess
DOI:10.1890/07-0539.1

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页码: 2783-2792
被引频次: 1419
出版者: Ecological Society of America,ECOLOGICAL SOC AMER
期刊名称: Ecology
ISSN: 0012-9658
卷期: Volume:88    Issue:11
语言: English
摘要: 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.
相关主题: Trees, Logistic regression, Estimate reliability, Bird nesting, Invasive species, Ecological modeling, Forest ecology, Ecological invasion, Information classification, Ecology, species distribution models, LDA, partial dependence plots, random forests, logistic regression, machine learning, additive logistic regression, classification trees, SPECIES DISTRIBUTION, MODELS, LANDSCAPE, ECOLOGY, Uinta Mountains, Classification, Lava Beds National Monument, Statistical methods, Flowers & plants, Comparative analysis, Pacific Northwest, Models, Theoretical, Data Interpretation, Statistical, Demography, Species Specificity, Logistic Models, Models, Statistical, Trees - growth & development, Ecology - methods, Algorithms, Animals, Ecosystem, Population Density, Birds - growth & development, Population Dynamics, Plants, Identification and classification, Ecological research, Nomenclature, Methods, Botany,

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