Newer classification and regression tree techniques: Bagging and random forests for ecological prediction

Ecosystems

Volumn 9, Issue 2, 2006, Pages 181-199

  Prasad, Anantha M ^a   Iverson, Louis R ^a   Liaw, Andy ^b  

^a USDA FOREST SERVICE   (United States)

^b MERCK RESEARCH LABORATORIES   (United States)

Author keywords

Bagging Trees; Canadian Climate Centre (CCC); Classification and regression trees (CART); Data mining; Decision tree; Fuzzy Kappa; Kappa; Multivariate Adaptive Regression Splines (MARS); Predictive mapping; Random Forests; Regression Tree Analysis (RTA)

Indexed keywords

CLIMATE CHANGE; ECOLOGICAL MODELING; GEOGRAPHICAL DISTRIBUTION; STATISTICAL ANALYSIS; TREE;

NORTH AMERICA; UNITED STATES;

ACER; ACER SACCHARUM; FAGUS; FAGUS GRANDIFOLIA; PINUS TAEDA; QUERCUS ALBA; SACCHARUM HYBRID CULTIVAR;

EID: 33645330972     PISSN: 14329840     EISSN: 14350629     Source Type: Journal    
DOI: 10.1007/s10021-005-0054-1     Document Type: Article

References (68)

1. Abraham A, Steinberg D. 2001. MARS: Still an alien planet in soft computing? In: Alexandrov VN, Dongarra JJ, Juliano BA, Renner RS, Tan CJK, Eds. Lecture notes in computer science 2074. Berlin Heidelberg New York: Springer, p 235-244.
2. Baker FA. 1993. Classification and regression tree analysis for assessing hazard of pine mortality caused by Heterobasidion annosum. Plant Dis 77:136-9.
3. Boer GJ, Flato GM, Ramsden D. 2000. A transient climate change simulation with historical and projected greenhouse gas and aerosol forcing: projected climate for the 21st century. Clim Dyn 16:427-51.
4. Breiman L. 1996a. Bagging predictors. Mach Learn 24:123-40.
5. Breiman L. 1996b. Out-of-bag estimation. Technical report, Department of Statistics: University of California, Berkeley.
6. Breiman L. 2001. Random Forests. Mach Learn 45:5-32.
7. Breiman L (2002) Using models to infer mechanisms. IMS Wald Lecture 2. [online] URL: http://oz.berkeley.edu/users/breiman/wald2002-2.pdf.
8. Breiman L, Cutler A. 2003. Setting up, using, and understanding Random Forests v4.0. [online] URL: http://www.stat.berkeley.edu/users/breiman/rf.html.
9. Breiman L, Freidman J, Olshen R, Stone C. 1984. Classification and regression trees. Belmont (CA): Wadsworth, 358 p.
10. Buhlmann P, Yu B. 2002. Analyzing bagging. Ann Stat 30:927-61.
11. Chambers JM. 1998. Programming with data: a guide to the S language. Berlin Heidelberg New York: Springer, 469 p.
12. Chambers JM, Hastie TJ (1993) Statistical models in New York; S. Chapman & Hall, 608 p.
13. Chan JCW, Huang C, DeFries R. 2001. Enhanced algorithm performance for land cover classification using bootstrap aggregating (bagging). IEEE Trans Geosci Remote Sens 39(3):693-5.
14. Clark JS. 1998. Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord. Am Nat 152:204-24.
15. Clark LA, Pregibon D. 1992. Tree-based models. In: Chambers JM, Hastie TJ, Eds. Statistical models in S. Pacific Grove (CA): Wadsworth, p 377-419.
16. Davis MB. 1989. Lags in vegetation response to greenhouse warming. Clim Change 15:75-82.
17. De'ath G, Fabricius KE. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178-192.
18. Dobbertin M, Biging GS. 1998. Using the non-parametric classifier CART to model forest tree mortality. For Sci 44(4):507-16.
19. Environmental Systems Research Institute. 2001. Arc ver. 8.1.2. Environmental Systems Research Institute, Redlands (CA).
20. Franklin J. 1995. Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19:494-519.
21. Franklin J. 1998. Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. J Veg Sci 9:733-48.
22. Freidman JH. 1991. Multivariate adaptive regression splines. Ann Stat 19:1-141.
23. Freund Y. 1995. Boosting a weak learning algorithm by majority. Inf Comput 121:256-85.
24. Furlanello C, Neteler M, Merler S, Menegon S, Fontanari S, Donini A, Rizzoli A, Chemini C. 2003. GIS and the Random Forests predictor: integration in R for tick-borne disease risk assessment. In: Hornik K, Leisch F, Zeileis A, Eds. Proceedings of the 3rd international workshop on distributed statistical computing. Vienna, Austria, p 1-11.
25. Hagen A. 2002. Technical report: comparison of maps containing nominal data. RIVM project: MAP-SOR S/550002/01/RO, order no. 143699. Maastricht (The Netherlands): Research Institute for Knowledge Systems.
26. Hagen A. 2003. Fuzzy set approach to assessing similarity of categorical maps. Int J Geog Inf Sci 17(3):235-49.
27. Hansen M, Dubayah R, Defries R. 1996. Classification trees: an alternative to traditional land cover classifiers. Int J Remote Sens 17(5):1075-81.
28. Hansen MH, Frieswyk T, Glover JF, Kelly JF (1992) The Eastwide forest inventory data base: users manual. General technical report NC-151. St. Paul (MM): US Department of Agriculture, Forest Service, North Central Forest Experiment Station, 48 p.
29. Hawkins DM, Musser BJ. 1999. One tree or a forest? Alternative dendrographic models. Comput Sci Stat 30:534-42.
30. Hernandez JE, Epstein LD, Rodriguez MH, Rodriguez AD, Rejmankova E, Roberts DR. 1997. Use of generalized regression tree models to characterize vegetation favoring Anopheles albimanus breeding. J Am Mosq Control Assoc 13(1):28-34.
31. Higgins SI, Lavorel S, Revilla EE. 2003. Estimating plant migration rates under habitat loss and fragmentation. Oikos 101:354-66.
32. Hobbs RJ. 1994. Dynamics of vegetation mosaics: can we predict responses to global change?. Ecoscience 1(4):346-56.
33. Hothorn T, Lausen B, Benner A, Radespiel-Troger M. 2004. Bagging survival trees. Stat Med 23:77-91.
34. Iverson LR, Prasad AM. 1998. Predicting abundance of 80 tree species following climate change in the eastern United States. Ecol Mono 68:465-85.
35. Iverson LR, Prasad AM. 2002. Potential redistribution of tree species habitat under five climate change scenarios in the eastern US. For Ecol Manage 155(1-3):205-22.
36. Iverson LR, Prasad AM, Hale BJ, Sutherland EK 1999a. An atlas of current and potential future distributions of common trees of the eastern United States. General technical report NE-265. Northeastern Research Station, USDA Forest Service, 245 p.
37. Iverson LR, Prasad AM, Schwartz MW. 1999b. Modeling potential future individual tree-species distributions in the Eastern United States under a climate change scenario: a case study with Pinus virginiana. Ecol Mod 115:77-93.
38. Kittel TGF, Rosenbloom NA, Kaufman C, Royle JA, Daly C, Fisher HH, and others. 2000. VEMAP phase 2 historical and future scenario climate database. Oak Ridge (TN): ORNL Distributed Active Archive Center, Oak Ridge National Laboratory. [online] URL: http://www.daac.ornl.gov/.
39. Lees BG, Ritman K. 1991. Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments. Environ Manage 15:823-31.
40. Liaw A, Wiener M. 2002. Classification and regression by Random Forests. R News, 2/3:18-22. [online] URL http:// CRAN.R-project.org/doc/Rnews/.
41. Little EL. 1971. Atlas of United States trees; vol 1. Conifers and important hardwoods. Miscellaneous publication 1146. Washington (DC), US Department of Agriculture, Forest Service, 200 p.
42. Little EL. 1977. Atlas of United States Trees; vol 4. Minor eastern hardwoods. Miscellaneous publication 1342. Washington (DC): US Department of Agriculture, Forest Service, 230 p.
43. Malcolm JR, Markham A, Neilson RP, Garaci M. 2002. Estimated migration rates under scenarios of global climate change. J Biogeogr 29:835-49.
44. Map Comparison Kit. 2003. Research Institute for Knowledge Systems, Netherlands. http://www.riks.nl.
45. Meyer D, Leisch F, Hornik K. 2003. The support vector machine under test. Neurocomputing 55:59-71.
46. Michaelsen J, Schimel DS, Friedl MA, Davis FW, Dubayah RC. 1994. Regression tree analysis of satellite and terrain data to guide vegetation sampling and surveys. J Veg Sci 5:673-86.
47. Miller JR, Turner MG, Smithwick EAH, Dent CL, Stanley EH. 2004. Spatial extrapolation: the science of predicting ecological patterns and processes. BioScience 54(4):310-20.
48. Moisen GG, Frescino T. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecol Model 157:209-25.
49. Monserud RA, Leemans R. 1992. Comparing global vegetation maps with the Kappa statistic. Ecol Model 62:275-93.
50. Moore DE, Lees BG, Davey SM. 1991. A new method for predicting vegetation distributions using decision tree analysis in a geographic information system. J Environ Manage 15:59-71.
51. Munoz J, Felicisimo AM. 2004. Comparison of statistical methods commonly used in predictive modelling. J Veg Sci 15:285-92.
52. Peters A, Hothorn T, Lausen B. 2002. ipred: Improved predictors. R News, 2(2):22-6 [online] URL http://CRAN.R-project.org/ doc/Rnews/.
53. Pitelka LF, Plant Migration Workshop Group. 1997. Plant migration and climate change. Am Sci 85:464-73.
54. Pontius RG Jr. 2000. Quantification error versus location error in comparison of categorical maps. Photogram Eng Remote Sens 66(8):1011-16.
55. Power C, Simms A. 2001. Hierarchical fuzzy pattern matching for regional comparison of land use maps. Int J Geogr Inf Sci 15(1):77-100.
56. Prasad AM, Iverson LR. 2000a. A climate change atlas for 80 forest tree species of the eastern United States [database]. [online] URL: http://www.fs.fed.us/ne/delaware/atlas.
57. Prasad AM, Iverson LR. 2000b. Predictive vegetation mapping using a custom built model-chooser: comparison of regression tree analysis and multivariate adaptive regression splines. In: Proceedings CD-ROM. 4th International Conference on Integrating GIS and Environmental Modeling: Problems, Prospects and Research Needs. Banff, Alberta, Canada. [online] URL: http://www.colorado.edu/research/cires/banff/upload/159/ index.html.
58. Prasad AM, Iverson LR. 2003. Little's range and FIA importance value database for 135 eastern US tree species. Northeastern Research Station, USDA Forest Service, Delaware, Ohio. [online] URL: http://www.fs.fed.us/ne/delaware/ 4153/global/ littlefia/index.html.
59. R Development Core Team. 2004. R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing. [online] URL: http://www. R-project.org.
60. Reichard SH, Hamilton CW. 1997. Predicting invasion of woody plants introduced into North America. Conserv Biol 11: 193-203.
61. Schapire RE, Freund Y, Barlett P, Lee W. 1998. Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651-86.
62. Schwartz MW, Iverson LR, Prasad AM. 2001. Predicting the potential future distribution of four tree species in Ohio, USA, using current habitat availability and climatic forcing. Ecosystems 4:568-81.
63. Skurichina M, Duin RPW. 2002. Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121-35.
64. Steinberg D, Colla PL, Martin K. 1999. MARS user guide. San Diego (CA): Salford Systems.
65. Stoppiana D, Gregoire J-M, Pereira JMC. 2003. The use of SPOT VEGETATION data in a classification tree appproach for burnt area mapping in Australian savanna. Int J Remote Sens 24:2131-51.
66. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. 2003. Random Forests: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947-58.
67. Therneau TM, Atkinson EJ. 1997. An introduction to recursive partitioning using the RPART routines. Technical report no. 61. Rochester (MM): Mayo Clinic, 52 p.
68. Verbyla DL. 1987. Classification trees: a new discrimination tool. Can J For Res 17:1150-2.