Sources of uncertainty in ecological modelling: Predicting vegetation types from environmental attributes

Community Ecology

Volumn 5, Issue 2, 2004, Pages 203-225

  Dale, M B ^a   Dale, P E R ^a  

^a GRIFFITH UNIVERSITY   (Australia)

Author keywords

Decision trees; MML; Salt marsh; Types of uncertainty; Vegetation types

Indexed keywords

ECOLOGICAL MODELING; SALTMARSH; UNCERTAINTY ANALYSIS; VEGETATION TYPE;

EID: 10944271707     PISSN: 15858553     EISSN: None     Source Type: Journal    
DOI: 10.1556/ComEc.5.2004.2.9     Document Type: Article

References (88)

1. Adomavicius, G. and A. Tuzhilin. 1997. Discovery of actionable patterns in databases: the action hierarchy approach. In: D. Heckerman, H. Mannila, D. Pregibon and R. Uthurusamy (eds.), Proc. 3rd International Conference on Knowledge Discovery and Data Mining. AAAI. pp. 111-114.
2. Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In: B. Petrov and F. Cáski (eds.) 2nd International Symposium on Information Theory, Akadémiai Kiadó, Budapest, pp. 267-281.
3. Allwein, E.L., R. E. Schapire and Y. Singer. 2000. Reducing multiclass to binary: a unifying approach for margin classifiers. J. Machine Learning Research 1:113-141.
4. Baxter, J. 2000. A model of inductive bias learning. J. Artif. Intell. Res. 12:149-198.
5. Beeston, G. R. and M. B. Dale. 1975. Multiple predictive analysis: a management tool. Proceedings of the Ecological Society of Australia 9:172-181.
6. Bengio, Y. and Y. Grandvalet 2003. No unbiased estimator of the variance of k-fold cross-validation. Technical Report 1234, Dept. Inform. Rech. Opérat., Université Montréal.
7. Boerlijst, M. C. and P. Hogeweg. 1991. Spiral wave structure in prebiotic evolution: hypercycles stable against parasites. Physica D 48:17-28.
8. Breiman, L., J. H. Friedman, R. A. Orshen and C. J. Stone. 1984. Classification and Regression Trees. Belmont CA. Wadsworth.
9. Brézillon P. and J-Ch. Pomerol. 1999. Contextual knowledge sharing and cooperation in intelligent assistant systems. Le Travail Humain 62:223-246 PUF, Paris.
10. Brown, D. E. and C. L. Pittard. 1993. Classification trees with optimal multivariate splits. Proc. International Conf. on Systems, Man and Cybernetics: Systems Engineering in the Service of Humans. I. E. E. E. New York, Vol 3. pp. 475-477.
11. Carley, K. and M. Palmquist. 1992. Extracting, representing and analyzing mental models. Social Forces 70:601-636.
12. Chatfield, C. 1995. Model uncertainty, data mining and statistical-inference J. R. Statist. Soc. Series A, 158:419-466.
13. Chipman, H., E. I. George and R. E. McCullough. 2000. Hierarchical priors for Bayesian CART shrinkage. Statistics and Computing 10:17-24.
14. Chipman, H., E. I. George and R. E. McCullough. 2001. The practical implementation of Bayesian model selection. IMS Lecture Notes: Monograph Ser. 38:67-136.
15. Czárán, T., R. F. Hoekstra. and L. Pagie. 2002. Chemical warfare between microbes promotes biodiversity. Proc. Natl. Acad. Sci. 99:786-790.
16. Dale, M. B. 1982. Strategy and tactics in pattern analysis: a response to Harrington, Dawes and Ludwig. Austral. J. Ecology 7:411-414.
17. Dale, M. B, 1999. The dynamics of diversity: mixed strategy systems Coenoses 13:105-113.
18. Dale, P. E. R. and M. B. Dale. 2002. Optimal classification to describe environmental change: pictures from the exposition. Community Ecology 3:19-29.
19. Dale, M. B., P. E. R. Dale and T. Edgoose. 2002a. Using Markov models to incorporate serial dependence in studies of vegetation change. Acta Oecologica 23:261-269.
20. Dale, M. B., P. E. R. Dale, C. Li and G. Biswas. 2002b. Assessing impacts of small perturbations using a model-based approach. Ecological Modelling 156:185-199.
21. Daley, R., 1977. On the inference of optimal descriptions. Theor. Comput. Sci. 4:301-319.
22. De'ath, Glenn, 1999: Principal Curves: A new technique for indirect and direct gradient analysis. Ecology 80:2237-2253.
23. Dasarathy, B. V. and A. L. Lakshminarasimhan. 1976. Sequential learning employing unfamiliar teacher hypothesis (SLEUTH) with concurrent estimation of both parameters and teacher characteristics, International J. Computer Information Sciences 5:1-7.
24. Esposito, F., D. Malerbo and G. Semeraro. 1995. Simplifying decision trees by pruning and grafting: new results. In: N. Larraç and S. Wrobel (eds.), Proc. 8th European Conf. Machine Learning, Lecture Notes in Artificial Intelligence 912 Springer, Berlin. pp. 287-290.
25. Fisher, D. 1992. Pessimistic and optimistic induction. Technical Report CS-92-12 Dept. Comput. Sci., Vanderbilt Univ.
26. Fitzgibbon, L. J., D. L. Dowe and L. Allison. 2002. Univariate polynomial inference by Monte Carlo Message Length approximation. In: C. Sammut and A. Hoffman (eds.), Proc. 19th Internatl. Conf. Machine Learning (ICML-2002), Morgan-Kauffman, San Francisco. pp. 147-154.
27. Forster, M. R. 2000. Key concepts in model selection: performance and generalizability. J. Math. Psychol: 44:205-231.
28. Freund, Y. and L. Mason. 1999. The alternating decision tree learning algorithm. In: Proc 16th Int. Conf. Machine Learning, Morgan-Kaufman, San Francisco. pp. 124-133.
29. Friedman, J. H., R. Kohavi and Y. Yun. 1996. Lazy decision trees. in: Proceedings of the 13th National Conference on Artificial Intelligence and Eighth Innovative Applications of Artificial Intelligence Conference, AAAI96, IAAI96 AAAI Press / The MIT Press. pp. 717-724.
30. Fürnkranz, J. 1996. Separate and conquer rule learning. Technical Report TR-96-25. Austrian Research Inst. Artificial Intell., Vienna.
31. Hájek, P., I. Havel and M. Chytil. 1966. The GUHA method of automatic hypotheses determination. Computing 1:293-308.
32. Hájek, P. and T. Havrének. 1977. On generation of inductive hypotheses. Internatl. d. Man-Mach. Stud. 9:415-438.
33. Hancock, T, T. Jiang., M. Li and J. Tromp. 1996. Lower bounds on learning decision lists and trees. Information and Computation 126:114-122.
34. Heckman, J. 1979. Sample selection bias as a specification error. Econometrika 47:153-161.
35. Heitjan, D. F. 1993. Ignorability and coarse data: some biomedical examples. Biometrics 49: 1099-1109.
36. Ho, T. K. 1998. The random subspace method for constructing decision forests. I. E. E. E. Trans. Pattern Analysis Machine Intelligence PAMI-20:832-844.
37. Hogeweg, P. 2002. Computing an organism: on the interface between informatic and dynamic processes. Biosystems 64:97-109.
38. Hunt, E. B., J. Marin, and P. J. Stone. 1966. Experimenta in Induction, Academic Press, New York.
39. Hyafil, L. and R. L. Rivest. 1976. Constructing optimal binary decision trees is NP-complete. Information Processing Letters 5:15-17.
40. Jelinski, D. E. and J-G. Wu. 1996. The modifiable areal unit problem and implications for landscape ecology. Landscape Ecology 11:129-140.
41. Jordan, M., Z. Ghahramani and L. K. Saul. 1997. Hidden Markov decision trees. In: M. C. Mozer, M. I. Jordan and T. Petsche (eds.), Advances in Neural Information Processing Systems 9. MIT Press Cambridge MA.
42. Lanterman, A. D. 2000. Schwarz, Wallace and Rissanen: Intertwining themes in theories of model order estimation, International Statistical Review 69:185 -212.
43. Li, C. G. Biswas, M. B. Dale and P. E. R. Dale. 2001. Building models of ecological dynamics using HMM based temporal data clustering. In: Advances in Intelligent Data Analysis, the 4th International Conference on Intelligent Data Analysis, Lecture Notes in Computer Science, 2189 Springer-Verlag, Berlin, pp. 53-62.
44. Loehle, C. 1987. Hypothesis testing in ecology: psychological aspects and the importance of theory maturation. Quarterly Review of Biology 62:397-409.
45. MacNally, R. 2000. Regression and model-building in conservation biology, biogeography and ecology: The distinction between - and reconciliation of- 'predictive' and 'explanatory' models. Biodiversity and Conservation 9:655-671.
46. Macnaughton-Smith, P. 1963. The classification of individuals by the possession of attributes associated with a criterion. Biometrica 19:364-366.
47. Macnaughton-Smith, P. 1965. Some statistical and other numerical methods for classifying individuals. Home Office Research Unit Report 6, Home Office, London.
48. Mehta, M., J. Rissanen and R. Agrawal. 1995. MDL-Based Decision Tree Pruning. Proc. 1st International Conf. Knowledge Discovery and Data Mining (KDD'95), pp. 216-221.
49. Mingers, J. 1989. An empirical comparison of selection measures for decision tree induction. Machine Learning 3:319-342.
50. Mikkelson, G. M. 2001. Complexity and verisimilitude: realism in ecology. Biology and Philosophy 16:533-546.
51. Murphy, P. M. and M. J. Pazzani. 1991. ID2-of-3: Constructive induction of M-of-N concepts for discriminators in decision trees. Proc. 8th International workshop Machine Learning, Morgan Kaufmann, San Mateo, CA. pp. 183-187.
52. Murthy, S., S. Kasif and S. Salzberg. 1994. A system for induction of oblique decision trees, J. Artif. Intell. Res. 2:1-32.
53. Oates, T. and D. Jensen. 1999. Toward a Theoretical Understanding of Why and When Decision Tree Pruning Algorithms Fail. Proc. 16th National Conf. Artificial Intelligence and 11th Conf. Innovative Applications of Artificial Intelligence, AAAI Press / The MIT Press. pp. 372-378.
54. Oliver, J. and Baxter, R. 1994. MML and Bayesianism: similarities and differences. Technical Report 206, Department of Computer Science, Monash University.
55. Oliver, J. and Hand, D. 1996. Averaging over decision trees. J. Classif. 13:281-297.
56. O'Neill, R. V., D. L. DeAngelis, J. B. Waide and T. F. H. Allen. 1986. A Hierarchical Concept of Ecosystems, Princeton Univ. Press, Princeton, N.J.
57. Opitz, D. and R. Machin. 1999. Popular ensemble methods: an empirical study, J. Artif. Intell. Res. 11:160-198.
58. Picard, R. R. and K. N. Berk. 1990. Data Splitting. American Statistician 44:140-147.
59. Posse, C. 1995a. Tools for two-dimensional exploratory projection pursuit, J. Computer Graphics Statist. 4:83-100.
60. Posse, C. 1995b. Projection pursuit exploratory data analysis. Computat. Statist. Data Anal. 20:669-687.
61. Quinlan, J. R. 1993. C4.5: Programs for Machine Learning, Morgan Kaufmann: San Mateo, CA.
62. Ramsey, J. B. and H-J. Yuan. 1990. The statistical properties of dimension calculations. Nonlinearity 3:155-176.
63. Rastetter, E. B., A. W. King, B. J. Cosby, G. M. Homberger, R. V. O'Neill and J. E. Hobbie. 1992. Aggregating fine-scale ecological knowledge to model coarser-scale attributes of ecosystems. Ecological Applications 2:55-70.
64. Riddle, B. R. and D. J. Hafner. 1999. Species as units of analysis in ecology and biogeography: time to take the blinkers off. Global Ecol. Biogeog. 8:433-441.
65. Robnik-Sikonja, M. and I. Kononenko. 1998. Pruning Regression Trees with MDL. In: Henri Prade (Ed.): Proc. 13th European Conf. Artificial Intelligence, Brighton, UK, August 23-28 1998. John Wiley and Sons, Chichester. pp. 455-459.
66. Rymon, R. 1993. An SE-tree based characterization of the induction problem. Proceedings of the 10th International Conference on Machine Learning, ML-93, Morgan Kaufmann, CA, pp. 268-275.
67. Schwarz, G. 1978. Estimating dimension of a model. Ann. Statist. 6: 461-464.
68. Srikant, R. and R. Agrawal. 1995. Mining generalized association rules. Res. Rep. RJ9963, IBM Almaden Res. Cent. San José, CA. and Proc. 21st Very Large Data Base Conference, Zurich, 13 pp.
69. Stone M. 1977. An Asymptotic Equivalence of Choice of Model by Cross-Validation and Akaike's Criterion. J. R. Stat. Soc., B 38:44-47.
70. Tan, P. J. and D. L. Dowe. 2002. MML inference of decision graphs with multi-way joins. Lecture Notes in Artificial Intelligence 2557, pp. 131-142.
71. Tan, P. J. and D. L. Dowe. 2003. MML inference of decision graphs with multi-way joins and dynamic attributes. Proc. 16th Australian Joint Conference on Artificial Intelligence, Perth, Dec. (to appear).
72. Ting, K. M. 1994. The problem of small disjuncts: its remedy in decision trees. Proc 10th Canadian Conf. Artif. Intell. pp. 91-97.
73. Todorovski, L. and S. Dzeroski. 2000. Combining multiple models with meta decision trees. Principles of Data Mining and Knowledge Discovery, pp. 54-64.
74. Tothill, J. C., J. N. G. Hargreaves and R. M. Jones. 1978. - BOTANAL - a comprehensive sampling and computing procedure for estimating pasture yield and composition. I. Field sampling. Technical Memorandum 8, CSIRO, Division Tropical Crops Pastures, Brisbane.
75. Utgoff, P. E. 1994. An improved algorithm for incremental induction of decision trees, In: W. Cohen and H. Hirsh (eds.), Proceedings of the 11th International Conference on Machine Learning, New Brunswick, NJ. Morgan Kaufmann. pp. 318-325.
76. van den Bosch, A., A. Weijters, H. J. Van der Herik. and W. Daelemans. 1997. When small disjuncts abound, try lazy learning: a case study. Proc. 7th Belgian-Dutch Conf. Machine Learning, pp. 109-118.
77. van de Velde, W. 1990. Incrementai induction of topologically minimal decision trees. In: Proceedings of the 7th International Conference on Machine Learning, Austin, TX. Morgan Kaufmann. pp. 66-74.
78. Vapnik V.N. 1995. The Nature of Statistical Learning Theory. Springer, Berlin.
79. Wallace, C. S. 1996. MML inference of predictive trees, graphs and nets. In: A. Gammerman (ed.), Computational Learning and Probabilistic Reasoning, John Wiley. pp. 43-66.
80. Wallace, C. S. and D. L. Dowe. 1999. Minimum Message Length and Kolmogorov complexity. Comput. J. 42:270-283.
81. Wallace, C. S. and J. Patrick. 1993. Coding decision trees. Machine Learning 11:7-22.
82. Webb, G. I. 1994. Generality is more significant than complexity: toward alternatives to Occam's razor. In: C. Zhang, J. Debenham and D. Lukose (eds), AI'94 - Proc. 7th Australian Joint Conf. Artificial Intelligence, Armidale, World Scientific. pp. 60-67.
83. Widmer, G. and M. Kubat. 1996. Learning in the presence of concept drift and hidden contexts. Machine Learning 23:69-101.
84. Williams, G. J. 1988 Combining decision trees. In: J. S. Gero and R. Stanton (eds.), Artifiical Intelligence: Developments and Applications. Elsevier Scientific, Amsterdam. pp. 273-289.
85. Wittgenstein, L. 1995. Tractacus 5:3651
86. Yamada, H. and S. Amaroso. 1971. Structural and behavioural equivalences of tessellation automata. Information and Control 18:1-31.
87. Young, P., S. Parkinson and M. Lees. 1996. Simplicity out of complexity in environmental modeling: Occam's razor revisited. J. Appl. Statist. 234:165-210.
88. Zhang, J., Y-S. Yim and J. Yang. 1997. Intelligent selection of instances for prediction function in lazy learning algorithms. Artif. Intel. Rev. 11:175-192.