Data-mining discovery of pattern and process in ecological systems

Journal of Wildlife Management

Volumn 71, Issue 7, 2007, Pages 2427-2437

  Hochachka, Wesley M ^a   Caruana, Rich ^b   Fink, Daniel ^a   Munson, Art ^b   Riedewald, Mirek ^b   Sorokina, Daria ^b   Kelling, Steve ^a  

^a Fuller Evolutionary Biology Program   (United States)

^b Department of Computer Science and School of Operations Research and Information Engineering   (United States)

Author keywords

Bagging; Data mining; Decision trees; Exploratory data analysis; Hypothesis generation; Machine learning; Prediction

Indexed keywords

DATA MINING; ESTIMATION METHOD; HYPOTHESIS TESTING; PREDICTION; STATISTICAL ANALYSIS;

EID: 48249133952     PISSN: 0022541X     EISSN: None     Source Type: Journal    
DOI: 10.2193/2006-503     Document Type: Article

References (30)

1. Bauer, E., and R. Kohavi. 1999. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36:105-139.
2. Breiman, L. 1996. Bagging predictors. Machine Learning 24:123-140.
3. Breiman, L. 2001. Random forests. Machine Learning 45:5-32.
4. Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and regression trees. Chapman & Hall, New York, New York, USA.
5. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretic approach. Second edition. Springer-Verlag, New York, New York, USA.
6. Caruana, R., M. Elhawary, A. Munson, M. Riedewald, D. Sorokina, D. Fink, W. M. Hochachka, and S. Kelling. 2006. Mining citizen science data to predict prevalence of wild bird species. Pages 909-915 in L. Ungar, M. Craven, D. Gunopulos, and T. Eliassi-Rad, editors. Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York, New York, USA.
7. Caruana, R., and A. Niculescu-Mizil. 2006. An empirical comparison of supervised learning algorithms. Pages 161-168 in W. W. Cohen and A. Moore, editors. Proceedings of the 23rd international conference on machine learning. ACM Press, New York, New York USA.
8. Cristianini, N., and J. Shawe-Taylor. 2000. An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, United Kingdom.
9. De'ath, G., and K. E. Fabricius. 2000. Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178-3192.
10. Elith, J., C. H. Graham, R. P. Anderson, M. Dudik, S. Ferrier, A. Guisan, R. J. Hijmans, F. Huettmann, J. R. Leathwick, A. Lehmann, J. Li, L. G. Lohmann, B. A. Loiselle, G. Manion, G. Moritz, M. Nakamura, Y. Nakazawa, J. McC. Overton, A. T. Peterson, S. J. Phillips, K. Richardson, R. Scachetti-Pereira, R. E. Schapire, J. Soberón, S. Williams, M. S. Wisz, and N. E. Zimmermann. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29:129-151.
11. Freund, Y., and R. E. Schapire. 1996. Experiments with a new boosting algorithm. Pages 148-156 in L. Saitta, editor. Machine learning: proceedings of the thirteenth international conference. Morgan Kauffman, San Francisco, California, USA.
12. Friedman, J. 2001. Greedy function approximations: a gradient boosting machine. Annals of Statistics 29:1189-1232.
13. Friedman, J., T. Hastie, and R. Tibshirani. 2000. Additive logistic regression: a statistical view of boosting. Annals of Statistics 28:337-374.
14. Friedman, J. H., and B. E. Popescu. 2005. Predictive learning via rule ensembles. Technical Report. Stanford University Department of Statistics technical report, Palo Alto, California, USA.
15. Harrell, F. E., Jr. 2001 Regression modeling strategies with applications to linear models, logistic regression and survival analysis. Springer Verlag, New York, New York, USA.
16. Hastie, T., and R. Tibshirani. 1990. Generalized additive models. Chapman and Hall, London, United Kingdom.
17. Hastie, T., R. Tibshirani, and J. Friedman. 2001. The elements of statistical learning: data mining, inference, and prediction. Springer Verlag, New York, New York, USA.
18. Hochachka, W. M., and A. A. Dhondt. 2000. Density-dependent decline of host abundance resulting from a new infectious disease. Proceedings of the National Academy of Sciences USA 97:5303-5306.
19. Hochachka, W. M., and A. A. Dhondt. 2006. House finch (Carpodacus mexicanus) population- and group-level responses to a bacterial disease. Ornithological Monographs 60:30-43.
20. Hooker, G. 2004. Discovering additive structure in black box functions. Pages 569-574 in R. Kohavi, J. Gehrke, W. DuMouchel, and J. Ghosh, editors. Proceedings of the 10th SIGKDD international conference on knowledge discovery and data mining. ACM Press, New York New York, USA.
21. Hosmer, D. W., and S. Lemeshow. 2000. Applied logistic regression. Second edition. John Wiley and Sons, New York, New York, USA.
22. Hosseini, P. R., A. A. Dhondt, and A. P. Dobson. 2006. Spatial spread of an emerging infectious disease: conjunctivitis in house finches - seasonal rates and geographic barriers. Ecology 87:3037-3046.
23. Jensen, F. V. 1996. An introduction to Bayesian networks. Springer Verlag, New York New York, USA.
24. King, R., and S. P. Brooks. 2004. Bayesian analysis of the Hector's dolphin data. Animal Biodiversity and Conservation 27:343-354.
25. Lepage, D., and C. M. Francis. 2002. Do feeder counts reliably indicate bird population changes? 21 years of winter bird counts in Ontario, Canada. Condor 104:255-270.
26. Mitchell, T. 1997. Machine learning. McGraw-Hill, New York, New York, USA.
27. Peters, R. H. 1991. A critique for ecology. Cambridge University Press, Cambridge, United Kingdom.
28. Wells, J. V., K. V. Rosenberg, E. H. Dunn, D. L. Tessaglia-Hymes, and A. A. Dhondt. 1998. Feeder counts as indicators of spatial and temporal variation in winter abundance of resident birds. Journal of Field Ornithology 69:577-586.
29. White, G. C., and K. P. Burnham. 1999. Program MARK: survival estimation from populations of marked animals. Bird Study 46(Supplement):120-138.
30. Wood, S. N. 2006. Generalized additive models: an introduction with R. Chapman and Hall/CRC, Boca Raton, Florida, USA.