Supervised pre-processing approaches in multiple class variables classification for fish recruitment forecasting

Environmental Modelling and Software

Volumn 40, Issue , 2013, Pages 245-254

  Fernandes, Jose A ^a,b   Lozano, Jose A ^b   Inza, Iñaki ^b   Irigoien, Xabier ^a,c   Pérez, Aritz ^b   Rodríguez, Juan D ^b  

^a MARINE RESEARCH DIVISION   (Spain)

^b UNIVERSITY OF THE BASQUE COUNTRY UPV EHU   (Spain)

^c KING ABDULLAH UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Saudi Arabia)

Author keywords

Bayesian networks; Discretization; Environmental modelling; Feature subset selection; Missing imputation; Multi dimensional classification; Recruitment forecasting; Supervised classification

Indexed keywords

DISCRETIZATIONS; ENVIRONMENTAL MODELLING; FEATURE SUBSET SELECTION; MISSING IMPUTATION; SUPERVISED CLASSIFICATION;

BAYESIAN NETWORKS; FEATURE EXTRACTION; FISH;

FORECASTING;

BAYESIAN ANALYSIS; DATA SET; ENVIRONMENTAL MODELING; FISH; FISHERY MANAGEMENT; IMAGE CLASSIFICATION; LEARNING; PROBABILITY; RECRUITMENT (POPULATION DYNAMICS);

ENGRAULIDAE; REXEA SOLANDRI;

EID: 84871748990     PISSN: 13648152     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.envsoft.2012.10.001     Document Type: Article

References (85)

1. Aguilera P., Fernández A., Fernández R., Rumí R., Salmerón A. Bayesian networks in environmental modelling. Environ. Model. Softw. 2011, 26(12):1376-1388.
2. Aguilera P., Fernández A., Reche F., Rumí R. Hybrid Bayesian network classifiers: application to species distribution models. Environ. Model. Softw. 2010, 25(12):1630-1639.
3. Ali S., Smith K. On learning algorithm selection for classification. Appl. Soft. Comput. 2006, 6(2):119-138.
4. Andonegi E., Fernandes J., Quincoces I., Irigoien X., Uriarte A., Pérez A., Howell D., Stefánsson G. The potential use of a gadget model to predict stock responses to climate change in combination with Bayesian networks: the case of bay of biscay anchovy. ICES J. Mar. Sci. 2011, 68(6):1257-1269.
5. Aqorau T. Obligations to protect marine ecosystems under international conventions and other legal instruments. Responsible Fisheries in the Marine Ecosystem 2003, 25-40. FAO Rome and CABI Publishing. M. Sinclair, G. Valdimarsson (Eds.).
6. Azamathulla H., Ghani A., Chang C., Hasan Z., Zakaria N. Machine learning approach to predict sediment load - a case study. CLEAN 2010, 38(10):969-976.
7. Azamathulla H., Wu F. Support vector machine approach for longitudinal dispersion coefficients in natural streams. Appl. Soft Comput. 2011, 11(2):2902-2905.
8. Bengio Y., Grandvalet Y. No unbiased estimator of the variance of K-fold cross-validation. J. Mach. Learn. Res. 2004, 5:1089-1105.
9. Bielza, C., Li, G., Larrañaga, P., 2010. Multi-dimensional Classification with Bayesian Networks. Technical Report, Department of Artificial Intelligence, Polytechnic University of Madrid, UPM-FI/DIA/2010-1, Madrid, Spain.
10. Bielza C., Li G., Larranaga P. Multi-dimensional classification with Bayesian networks. Int. J. Approx. Reason 2011, 52(6):705-727.
11. Blanco R., Inza I., Larrañaga P. Learning Bayesian networks in the space of structures by estimation of distribution algorithms. Int. J. Intell. Syst. 2003, 18(2):205-220.
12. Botsford L., Castilla J., Peterson C. The management of fisheries and marine ecosystems. Science 1997, 277(5325):509-515.
13. Bouckaert R.R., Frank E. Evaluating the replicability of significance tests for comparing learning algorithms. Lect. Notes Comput. Sci. 2004, 3-12.
14. Brier G.W. Verification of forecasts expressed in terms of probability. Mon. Weather Rev. 1950, 78(1):1-3.
15. Caruana R. Multi-task learning. Mach. Learn. 1997, 28:41-75.
16. Catlett J. On changing continuous attributes into ordered discrete attributes. Proceedings of the European Working Session on Learning 1991, 164-178.
17. Cesa-Bianchi N., Gentile C., Zaniboni L. Incremental algorithms for hierarchical classification. J. Mach. Learn. Res. 2006, 7:31-54.
18. Chen S., Pollino C. Good practice in Bayesian network modelling. Environ. Model. Softw. 2012, 37:134-145.
19. Correa M., Bielza C., Pamies-Teixeira J. Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process. Expert Syst. Appl. 2009, 36(3):7270-7279.
20. Cury P., Shannon L., Shin Y. The functioning of marine ecosystems: a fisheries perspective. Responsible Fisheries in the Marine Ecosystem 2003, 343-361. FAO Rome and CABI Publishing. M. Sinclair, G. Valdimarsson (Eds.).
21. Daumé H., Marcu D. Learning as search optimization: approximate large margin methods for structured prediction. International Conference in Machine Learning 2005, 169-176.
22. de Waal P.R., van der Gaag L.C. Inference and learning in multi-dimensional Bayesian network classifiers. Lect. Notes Comput. Sci. 2007, 4724:501-511.
23. Delavallade T., Dang T.H. Using entropy to impute missing data in a classification task. Proceedings of the IEEE International Conference on Fuzzy Systems 2007, vol. 7.
24. Dem̌sar J. Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 2006, 7:1-30.
25. Dreyfus-León M., Chen D.G. Recruitment prediction with genetic algorithms with application to the Pacific Herring fishery. Ecol. Model. 2007, 203(1-2):141-146.
26. Edwards S.F., Link J.S., Rountree B.P. Portfolio management of wild fish stocks. Ecol. Econ. 2004, 49(3):317-329.
27. Essington T.E. The precautionary approach in fisheries management: the devil is in the details. Trends Ecol. Evol. 2001, 16(3):121-122.
28. Fayyad U., Irani K. Multi-interval discretization of continuous-valued attributes for classification learning. Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence 1993, 1022-1027.
29. Fayyad U.M., Piatetsky-Shapiro G., Smyth P. From data mining to knowledge discovery: an overview. Advances in Knowledge Discovery and Data Mining 1996, 1-24. AAAI/MIT. American Association for Artificial Intelligence Menlo Park, CA, USA. U.M. Fayyad (Ed.).
30. Fernandes J., Irigoien X., Uriarte A., Ibaibarriaga L., Lozano J., Inza I. Anchovy Recruitment Mixed Long Series Prediction Using Supervised Classification. Tech. Rep. 2009, Working Document to the ICES Benchmark Workshop on Short Lived Species (WKSHORT), Bergen, Norway.
31. Fernandes J.A., Irigoien X., Boyra G., Lozano J.A., Inza I. Optimizing the number of classes in automated zooplankton classification. J. Plankton Res. 2009, 31(1):19-29.
32. Fernandes J., Kauppila P., Uusitalo L., Fleming-Lehtinen V., Kuikka S., Pitkanen H. Evaluation of reaching the targets of the water framework directive in the Gulf of Finland. Environ. Sci. Technol. 2012, 46(15):8220-8228.
33. Fernandes J.A., Irigoien X., Goikoetxea N., Lozano J.A., Inza I., Pérez A., Bode A. Fish recruitment prediction, using robust supervised classification methods. Ecol. Model. 2010, 221(2):338-352.
34. Fortier L., Villeneuve A. Cannibalism and predation on fish larvae by larvae of Atlantic mackerel, Scomber scombrus: trophodynamics and potential impact on recruitment. Fish. Bull. 1996, 94(2):268-281.
35. Fulton E.A., Smith A.D.M., Johnson C.R. Effect of complexity on marine ecosystem models. Mar. Ecol. Prog. Ser. 2003, 253:1-16.
36. García S., Cochrane K. Ecosystem approach to fisheries: a review of implementation guidelines. ICES J. Mar. Sci. 2005, 62(3):311-318.
37. García S., Herrera F. An extension on 'statistical comparisons of classifiers over multiple data sets' for all pairwise comparisons. J. Mach. Learn. Res. 2008, 9:2677-2694.
38. Geurts P., Wehenkel L. Investigation and reduction of discretization variance in decision tree induction. Proceedings of the Eleventh European Conference on Machine Learning 2000, 162-170.
39. Guyon I., Aliferis C., Elisseeff A. Causal feature selection. Computational Methods of Feature Selection 2007, 63-82.
40. Hall, M.A., 1999. Correlation-based Feature Selection for Machine Learning. Ph.D. thesis, Waikato University, New Zealand.
41. Hall M.A. Correlation-based feature selection of discrete and numeric class machine learning. Proceedings of the Seventeenth International Conference on Machine Learning 2000, 359-366.
42. Hill S., Watters G., Punt A., McAllister M., Quéré C., Turner J. Model uncertainty in the ecosystem approach to fisheries. Fish Fish 2007, 8(4):315-336.
43. Hollowed A.B., Bax N., Beamish R., Collie J., Fogarty M., Livingston P., Pope J., Rice J.C. Are multispecies models an improvement on single-species models for measuring fishing impacts on marine ecosystems?. ICES J. Mar. Sci. 2000, 57(3):707-719.
44. Hua J., Temble W.D., Dougherty E.R. Performance of feature-selection methods in the classification of high-dimensional data. Pattern Recogn. 2009, 42:409-424.
45. Ibaibarriaga L., Fernandez C., Uriarte A., Roel B. A two-stage biomass dynamic model for Bay of Biscay anchovy: a Bayesian approach. ICES J. Mar. Sci. 2008, 65(2):191-205.
46. Inza I., Larrañaga P., Sierra B., Etxeberria R., Lozano J.A., Peña J.M. Representing the behaviour of supervised classification learning algorithms by Bayesian networks. Pattern Recogn. Lett. 1999, 20(11-13):1201-1209.
47. Irigoien X. Reply to Horizons Article 'Castles built on sand: dysfunctionality in plankton models and the inadequacy of dialogue between biologists and modellers' Flynn (2005). Shiny mathematical castles built on grey biological sands. J. Plankton Res. 2006, 28(10):965.
48. Irigoien X., de Roos A. The role of intraguild predation in the population dynamics of small pelagic fish. Mar. Biol. 2011, 158(8):1683-1690.
49. John G.H., Langley P. Estimating continuous distributions in Bayesian classifiers. Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence 1995, vol. 1:338-345.
50. Kohavi R., Sommerfield D. Feature subset selection using the wrapper method: overfitting and dynamic search space topology. Proceedings of the First International Conference on Knowledge Discovery and Data Mining 1995, 192-197.
51. Kononenko I. On biases in estimating multi-valued attributes. Proceedings of the First International Joint Conference on Artificial Intelligence 1995, vol. 14:1034-1040.
52. Kononenko I., Bratko I., Roskar E. Experiments in automatic learning of medical diagnostic rules. International School for the Synthesis of Expertise Knowledge Workshop, Bled, Slovenia 1984.
53. Kotsiantis S. Supervised machine learning: a review of classification techniques. Informatica 2007, 31:249-268.
54. Kotsiantis S.B., Kanellopoulos D., Pintelas P.E. Data preprocessing for supervised leaning. Int. J. Comput. Sci. 2006, 1(2):1306-4428.
55. Leggett W.C., Deblois E. Recruitment in marine fishes: is it regulated by starvation and predation in the egg and larval stages?. Neth. J. Sea Res. 1994, 32(2):119-134.
56. Link J. (Re)constructing food webs and managing fisheries. Ecosystem Approaches for Fisheries Management. Proceedings of the 16th Lowell Wakefield Fisheries Symposium 1999, 571-588. Univ. Of Alaska Sea Grant, AK-99-01, Fairbanks.
57. Malekmohammadi P., Kerachian R., Zahraie B. Developing monthly operating rules for a cascade system of reservoirs: application of Bayesian Networks. Environ. Model. Softw. 2009, 24(12):1420-1432.
58. McAllister M.K., Ianelli J.N. Bayesian stock assessment using catch-age data and the sampling-importance resampling algorithm. Can. J. Fish. Aquat. Sci. 1997, 54(2):284-300.
59. Nadeau C., Bengio Y. Inference for the generalization error. Mach. Learn. 2003, 52(3):239-281.
60. Nash D., Hannah M. Using Monte-Carlo simulations and Bayesian networks to quantify and demonstrate the impact of fertiliser best management practices. Environ. Model. Softw. 2011, 26(9):1079-1088.
61. Newman K.B., Buckland S.T., Lindley S.T., Thomas L., Fernández C. Hidden process models for animal population dynamics. Ecol. Appl. 2006, 16(1):74-86.
62. Pérez A., Larrañaga P., Inza I. Information theory and classification error in probabilistic classifiers. Lect. Notes Comput. Sc 2006, 4265:347-351.
63. Planque B., Buffaz L. Quantile regression models for fish recruitment-environment relationships: four case studies. Mar. Ecol. Progr. Ser. 2008, 357:213-223.
64. Quevedo J., Luaces O.,A.,B. Multilabel classifiers with a probabilistic thresholding strategy. Pattern Recogn. 2011, 45(2):876-883.
65. Ragozin D.L., Brown G. Harvest policies and nonmarket valuation in a predator-prey system. J. Environ. Econ. Manage. 1985, 12(2):155-168.
66. Reunanen J. Overfitting in making comparisons between variable selection methods. J. Mach. Learn. Res. 2003, 3:1371-1382.
67. Ricker W.E. Stock and recruitment. J. Fish. Res. Board Can. 1954, 11:559-623.
68. Ricker W.E. Handbook of Computations for Biological Statistics of Fish Populations. Fish. Res. Bd. Canada Bull. 119 1958, 300 pp.
69. Rodríguez J., Lozano J. Multi-objective learning of multi-dimensional Bayesian classifiers. HIS'08. Eighth International Conference on. IEEE 2008, 501-506.
70. Rodríguez J.D., Lozano J.A. Multi-objective learning of multi-dimensional Bayesian classifiers. Proceedings of the Eighth International Conference on Hybrid Intelligent Systems 2008, 501-506.
71. Rodríguez J.D., Pérez A., Lozano J.A. Sensitivity analysis of k-fold cross-validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 2009, 32(3):569-575.
72. Ruckelshaus M., Klinger T., Knowlton N., de Master D. Marine ecosystem-based management in practice: scientific and governance challenges. Bioscience 2008, 58(1):53-63.
73. Saeys Y., Inza I., Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23(19):2507-2517.
74. Schirripa M.J., Colbert J.J. Interannual changes in sablefish (Anoplopoma fimbria) recruitment in relation to oceanographic conditions within the California Current System. Fish. Oceanogr. 2006, 15(1):25-36.
75. Sebastiani P., Abad M.M., Ramoni M.F. Bayesian networks. The Data Mining and Knowledge Discovery Handbook 2005, 193-230. Springer, New York, NY, USA. O. Maimom, O. Rokach (Eds.).
76. Statnikov A., Aliferis C.F., Tsamardinos I., Hardin D., Levy S. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 2005, 21(5):631-643.
77. Tax D.M.J., Duin R.P.W. Using Two-class Classifiers for Multiclass Classification. International Conference on Pattern Recognition 2002, vol. 2. pp. 124-127.
78. Tsoumakas G., Katakis I. Multi-label classification: an overview. Int. J. Data Warehouse. Min. 2007, 3(3):1-13.
79. Tucker A., Duplisea D. Bioinformatics tools in predictive ecology: applications to fisheries. Phil. Trans. R. Soc. B 2012, 367(1586):279-290.
80. Uusitalo L. Advantages and challenges of Bayesian networks in environmental modelling. Ecol. Model. 2007, 203(3-4):312-318.
81. van der Gaag L.C., de Waal P.R. Muti-dimensional Bayesian network classifiers. Proceedings of the Third European Workshop in Probabilistic Graphical Models 2006, 107-114.
82. Van der Gaag L.C., Renooij S., Witteman C.L.M., Aleman B.M.P., Taal B.G. Probabilities for a probabilistic network: a case study in oesophageal cancer. Artif. Intell. Med. 2002, 25(2):123-148.
83. Witten I.H., Frank E. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations 2005, Morgan Kaufmann, San Francisco.
84. Young W., Millie D., Weckman G., Anderson J., Klarer D., Fahnenstiel G. Modeling net ecosystem metabolism with an artificial neural network and Bayesian belief network. Environ. Model. Softw. 2011, 26(10):1199-1210.
85. Zaragoza J., Sucar L., Morales E. A two-step method to learn multidimensional Bayesian network classifiers based on mutual information measures. Twenty-fourth International FLAIRS Conference 2011.