A review of supervised machine learning algorithms and their applications to ecological data

Ecological Modelling

Volumn 240, Issue , 2012, Pages 113-122

  Crisci, C ^a   Ghattas, B ^b   Perera, G ^c  

^a CNRS   (France)

^b AIX MARSEILLE UNIVERSITY   (France)

^c UNIVERSIDAD DE LA REPÚBLICA   (Uruguay)

Author keywords

Classification rules; Coastal rocky benthic communities; Ecological data; Machine learning; Mass mortality events; Positive thermal anomalies; Prediction; Regression analysis

Indexed keywords

BENTHIC COMMUNITIES; CLASSIFICATION RULES; ECOLOGICAL DATA; MASS MORTALITY; THERMAL ANOMALIES;

ECOLOGY; FORECASTING; LEARNING SYSTEMS; REGRESSION ANALYSIS; SUPERVISED LEARNING;

LEARNING ALGORITHMS;

ALGORITHM; BENTHOS; COMMUNITY STRUCTURE; DATA SET; LITERATURE REVIEW; MACHINERY; PREDICTION; REGRESSION ANALYSIS; TEMPERATURE EFFECT;

MEDITERRANEAN SEA;

EID: 84862253560     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2012.03.001     Document Type: Review

References (60)

1. Ballesteros E. Mediterranean coralligenous assemblages: a synthesis of present knowledge. Oceanography and Marine Biology 2006, 44:123-195.
2. Bell J.F. Application of classification trees to habitat preference of upland birds. Journal of Applied Statistics 1996, 23:349-359.
3. Benito Garzón M., Blazek R., Neteler M., Sánchez de Dios R., Sainz Ollero H., Furlanello C. Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian peninsula. Ecological Modelling 2006, 197:383-393.
4. Bensoussan N., Romano J.C., Harmelin J.G., Garrabou J. High resolution characterization of northwest Mediterranean coastal waters thermal regimes: to better understand responses of benthic communities to climate change. Estuarine, Coastal and Shelf Science 2010, 87:431-441.
5. Breiman L. Bagging predictors. Machine Learning 1996, 24:123-140.
6. Breiman L. Heuristic of instability and stabilization in model selection. The Annals of Statistics 1996, 24(6):2350-2383.
7. Breiman L. Random forests. Machine Learning 2001, 45(1):5-32.
8. Breiman L., Friedman J., Stone C.J., Olshen R.A. Classification and Regression Trees 1984, Chapman & Hall/CRC.
9. Cerrano C., Bavestrello G., Bianchi C., Cattaneo-vietti R., Bava S., Morganti C., Morri C., Picco P., Sara G., Shiaparelli S., Siccardi A., Sponga F. A catastrophic mass mortality episode of gorgonians and other organisms in the Ligurian sea (North-Western Mediterranean), summer 1999. Ecology Letters 2000, 3:284-293.
10. Chaudhuri P., Lo W., Loh W., Yang C.C. Generalized regression trees. Statistica Sinica 1995, 17(3):641-666.
11. Crisci C., Besoussan N., Romano J., Garrabou J. Temperature anomalies and mortality events in marine communities: insights on factors behind differential mortality impacts in the NW Mediterranean. PLoS ONE 2011, 6:e23814. http://dx.doi.org/10.1371%2Fjournal.pone.0023814.
12. Cutler D.R., Edwards T.C.J., Beard K., Cutler A., Hess K.T., Gibson J., Lawler J.J. Random forest for classification in ecology. Ecology 2007, 88(11):2783-2792.
13. Davison A., Hinkley D. Bootstrap methods and their application. Cambridge Series in Statistical and Probabilistic Mathematics 1997, vol. 1. Cambridge University Press.
14. De'ath G. Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology 2002, 83(4):1105-1117.
15. De'ath G. Boosted trees for ecological modeling and prediction. Ecology 2007, 88(1):243-251.
16. De'ath G., Fabricius K.E. Classification and regression tress: a powerful yet simple technique for ecological data analyses. Ecology 2000, 81(11):3178-3192.
17. Defeo O., Gómez J. Morphodynamics and habitat safety in sandy beaches: life-history adaptations in a supralittoral amphipod. Marine Ecology Progress Series 2005, 293:143-153.
18. Džeroski S. Application of symbolic machine learning to ecological modelling. Ecological Modelling 2001, 146:263-273.
19. Devroye L., Gyorfi L., Lugosi G. A Probabilistic Theory of Pattern Recognition 1996, Springer.
20. Efron B., Tibshirani R.J. An Introduction to the Bootstrap May 1994, Chapman & Hall/CRC.
21. Fernandes J., Irigoien X., Goikoetxea N., Lozano J.A., Inza I., Pérez A., Bode A. Fish recruitment prediction, using robust supervised classification methods. Ecological Modelling 2010, 221:338-352.
22. Fielding A. Machine Learning Methods for Ecological Applications 1999, Springer.
23. Flach P. On the state of the art in machine learning: a personal review. Artificial Intelligence 2001, 13(1):199-222.
24. Franklin J. Predicting the distribution of shrub species in Southern California from climate and terrain-derived variables. Journal of Vegetation Science 1998, 9:733-748.
25. Freund Y. Boosting a weak learning algorithm by majority. Information and Computation 1995, 121(2):256-285.
26. Friedman J.H., Stuetzle W. Projection pursuit regression. Journal of the American Statistical Association 1981, 76(376):817-823.
27. Garrabou J., Coma R., Bensousson N., Chevaldonné P., Cigliano M., Diaz D., Harmelin J.G., Gambi M.C., Kersting D.K., Ledoux J.B., Lejeusne C., Linares C., Marschal C., Perez T., Ribes M., Romano J.C., Serrano E., Teixido N., Torrents O., Zabala M., Zuberer F., Cerrano C. Mass mortality in NW Mediterranean rocky benthic communities: effects of the 2003 heat wave. Global Change Biology 2009, 15:1090-1103.
28. Grubb T., Bowerman W. Variations in breeding bald eagle responses to jets, light planes and helicopters. Journal of Raptor Research 1997, 31:213-222.
29. Guisan A., Edwards T.C., Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecological Modelling 2002, 157(2):89-100.
30. Guruswami V., Sahai A. Multiclass learning, boosting, and error-correcting codes. COLT'99: Proceedings of the Twelfth Annual Conference on Computational Learning Theory 1999, 145-155. ACM Press, New York, NY, USA.
31. Hastie T., Tibshirani R. Generalized Additive Models 1990, Chapman & Hall/CRC.
32. Hastie T., Tibshirani R., Friedman J.H. The Elements of Statistical Learning 2003, Springer.
33. Huttmann F., Lock A. A new software system for the PIROP database: data flow and an approach for a seabird-depth analysis. ICES Journal of Marine Science 1997, 54(4):518-523.
34. Knudby A., Brenning A., LeDrew E. New approaches to modelling fish-habitat relationships. Ecological Modelling 2010, 221:503-511.
35. Kocev D., Džeroski S., White M., Newell G., Griffioen P. Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition. Ecological Modelling 2009, 220:1159-1168.
36. Kocev D., Naumoski A., Mitreski K., Krstić S., Džeroski S. Learning habitat models for the diatom community in lake Prespa. Ecological Modelling 2010, 221:330-337.
37. Lek S., Guégan J. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological Modelling 1999, 120:65-73.
38. Loh W., Vanichsetakul N. Tree-structured classification via generalized discriminant analysis. Journal of the American Statistical Association 1988, 83(403):715-728.
39. Merckx B., Goethals P., Steyaert M., Vanreusel A., Vincx M., Vanaverbeke J. Predictability of marine nematode biodiversity. Ecological Modelling 2009, 220:1449-1458.
40. Morris C.W., Autret A., Boddy L. Support vector machines for identifying organisms a comparison with strongly partitioned radial basis function networks. Ecological Modelling 2001, 146:57-67.
41. Nelder J., Wedderburn R. Generalized linear models. Journal of the Royal Statistical Society 1972, 135(3):370-384.
42. Nerini D., Ghattas B. Classifying densities using functional regression trees: applications in oceanology. Computational Statistics & Data Analysis 2007, 51(10):4984-4993.
43. Perez T., Garrabou J., Sartoretto S., Harmelin J.G., Francour P., Vacelet J. Mortalité massive d'invertébres marins: un événement sans précédent en méditerranée nord-occidentale. Comptes Rendus de l'Académie des Sciences (Paris) - Series III 2000, 323:853-865.
44. Phetkaew T., Kijsirikul B., Rivepiboon W. Multiclass classification of support vector machines by reordering adaptive directed acyclic graph. International Workshop on Intelligent Systems 2003.
45. Pontin D., Schliebs S., Worner S., Watts M. Determining factors that influence the dispersal of a pelagic species: a comparison between artificial neural networks and evolutionary algorithms. Ecological Modelling 2011, 222:1657-1665.
46. Ramsay J., Silverman B. Applied functional data analysis: methods and case studies. Springer Series in Statistics 2002, Springer.
47. Recknagel F. Applications of machine learning to ecological modelling. Ecological Modelling 2001, 146:303-310.
48. Ribeiro R., Torgo L. A comparative study on predicting algae blooms in Douro River, Portugal. Ecological Modelling 2008, 212:86-91.
49. Ribic C., Ainley D. The relationships of seabird assemblages to physical habitat features in pacific equatorial waters during spring 1984-1991. ICES Journal of Marine Science 1997, 54(4):593-599.
50. Romano J.C., Lugrezi M.C., Durand D., Durand-LeBreton F. Serie du marégraphe de marseille: mesures de témperatures de surface de la mer de 1895 á 1956: une correction. Comptes Rendus Geoscience 2010, 342(12):873-880.
51. Rosenblatt F. Principles of Neurodynamics: Perceptron and Theory of Brain Mechanisms 1962, Spartan Books.
52. Ryder T.J., Irwin L. Winter habitat relationships of pronghorns in Southcentral Wyoming. The Journal of Wildlife Management 1987, 51(1):79-85.
53. Segal M.R. Tree-structured methods for longitudinal data. Journal of the American Statistical Association 1992, 87(418):407-418.
54. Shan Y., Paull D., McKay R. Machine learning of poorly predictable ecological data. Ecological Modelling 2006, 195:129-138.
55. Taylor B.J. Methods and Procedures for the Verification and Validation of Artificial Neural Networks 2006, Springer.
56. Vapnik V. Statistical Learning Theory 1998, Wiley.
57. Volf G., Atanasova N., Kompare B., Precali R., Oani N. Descriptive and prediction models of phytoplankton in the northern adriatic. Ecological Modelling 2011, 222:2502-2511.
58. Yuu Y., Einoshin S., Hideto Y., Katsuhiko T. Decision-tree induction from time-series data based on a standard-example split test. Proceedings of the Twentieth International Conference on Machine Learning 2003.
59. Zhang H. Classification trees for multiple binary responses. Journal of the American Statistical Association 1998, 93(441):180193.
60. Zhu J., Rosset S., Zou H., Hastie T. Multi-class AdaBoost. Technical Report 2006, Standford University.