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Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks

Olden, Julian D, Jackson, Donald A·2002·Ecological Modelling
DOI 10.1016/s0304-3800(02)00064-9
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Research works printed but not linked: 39

  1. Anderson, J.A., 1995. An Introduction to Neural Networks. MIT, Cambridge, MA, 650 pp. · DOI · Paper page
  2. Aoki, 1999, Analysis and prediction of the fluctuation of sardine abundance using a neural network, Oceanol. Acta, 20, 81
  3. Aurelle, 1999, Microsatellites and artificial neural networks: tools for the discrimination between natural and hatchery brown trout (Salmo trutta, L.) in Atlantic populations, Ecol. Model., 120, 313, 10.1016/S0304-3800(99)00111-8 · DOI
  4. Bastarache, 1997, Predicting conductivity and acidity for small streams using neural networks, Can. J. Civ. Eng., 24, 1030
  5. Brosse, 1999, The use of neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake, Ecol. Model., 120, 299, 10.1016/S0304-3800(99)00110-6 · DOI
  6. Brosse, 2001, Abundance, diversity, and structure of freshwater invertebrates and fish communities: an artificial neural network approach, New Zealand J. Mar. Fresh. Res., 35, 135, 10.1080/00288330.2001.9516983 · DOI
  7. Chen, 1999, A neural network model for forecasting fish stock recruitment, Can. J. Fish. Aquat. Sci., 56, 2385, 10.1139/f99-178 · doi:10.1139/f99-178
  8. Cheng, 1994, Neural networks: a review from a statistical perspective (with discussion), Stat. Sci., 9, 2
  9. Colasanti, 1991, Discussions of the possible use of neural network algorithms in ecological modeling, Binary, 3, 13
  10. Dimopoulos, 1999, Neural network models to study relationships between lead concentration in grasses and permanent urban descriptors in Athens city (Greece), Ecol. Model., 120, 157, 10.1016/S0304-3800(99)00099-X · DOI · Paper page
  11. Edwards, 1995, The potential for computer-aided identification in biodiversity research, Trend Ecol. Evol., 10, 153, 10.1016/S0169-5347(00)89026-6 · doi:10.1016/s0169-5347(00)89026-6
  12. Garson, 1991, Interpreting neural-network connection weights, Artif. Intell. Expert, 6, 47
  13. Gelfand, 1991, Recursive stochastic algorithms for global optimization in Rd, SIAM J. Control Optimization, 29, 999, 10.1137/0329055 · doi:10.1137/0329055
  14. Goh, 1995, Back-propagation neural networks for modeling complex systems, Artif. Intell. Eng., 9, 143, 10.1016/0954-1810(94)00011-S · DOI · Paper page
  15. Gozlan, 1999, Predicting the structure and diversity of young-of-the-year fish assemblages in large rivers, Fresh. Biol., 41, 809, 10.1046/j.1365-2427.1999.00423.x · DOI
  16. Guégan, 1998, Energy availability and habitat heterogeneity predict global riverine fish diversity, Nature, 391, 382, 10.1038/34899 · DOI · Paper page
  17. Laë, 1999, Predicting fish yield of African lakes using neural networks, Ecol. Model., 120, 325, 10.1016/S0304-3800(99)00112-X · DOI
  18. Lek, 1995, Improved estimation, using neural networks, of the food consumption of fish populations, Mar. Freshwater Res., 46, 1229, 10.1071/MF9951229 · doi:10.1071/mf9951229
  19. Lek, 1996, Role of some environmental variables in trout abundance models using neural networks, Aquat. Living Resour., 9, 23, 10.1051/alr:1996004 · DOI
  20. Lek, 1996, Application of neural networks to modeling nonlinear relationships in ecology, Ecol. Model., 90, 39, 10.1016/0304-3800(95)00142-5 · DOI · Paper page
  21. Lek, 1999, Artificial neural networks as a tool in ecological modeling, an introduction, Ecol. Model., 120, 65, 10.1016/S0304-3800(99)00092-7 · DOI · Paper page
  22. Lek, S., Guégan, J.F. (Ed.), 2000. Artificial Neuronal Networks, Applications to Ecology and Evolution. Springer-Verlag, New York, USA. · doi:10.1007/978-3-642-57030-8
  23. Lek-Ang, 1999, Predictive models of collembolan diversity and abundance in a riparian habitat, Ecol. Model., 120, 247, 10.1016/S0304-3800(99)00106-4 · DOI
  24. Manel, 1999, Comparing discriminant analysis, neural networks and logistic regression for predicting species distributions: a case study with a Himalayan river bird, Ecol. Model., 120, 337, 10.1016/S0304-3800(99)00113-1 · DOI · Paper page
  25. Mastrorillo, 1997, Predicting the abundance of minnow Phoxinus phoxinus (Cyprinidae) in the River Ariege (France) using artificial neural networks, Aquat. Living Resour., 10, 169, 10.1051/alr:1997018 · doi:10.1051/alr:1997018
  26. Mastrorillo, 1997, The use of artificial neural networks to predict the presence of small-bodied fish in a river, Fresh. Biol., 38, 237, 10.1046/j.1365-2427.1997.00209.x · DOI
  27. Mastrorillo, 1998, Predicting local fish species richness in the Garonne River basin, C.R. Acad. Sci. Paris, Sciences de la vie, 321, 423
  28. Minns, 1989, Factors affecting fish species richness in Ontario lakes, Trans. Am. Fish. Soc., 118, 533, 10.1577/1548-8659(1989)118<0533:FAFSRI>2.3.CO;2 · doi:10.1577/1548-8659(1989)118<0533:fafsri>2.3.co;2
  29. Olden, 2000, An artificial neural network approach for studying phytoplankton succession, Hydrobiology, 436, 131, 10.1023/A:1026575418649 · DOI
  30. Olden, 2000, Torturing data for the sake of generality: how valid are our regression models?, Écoscience, 7, 501, 10.1080/11956860.2000.11682622 · DOI
  31. Olden, 2001, Fish-habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks, Trans. Am. Fish. Soc., 130, 878, 10.1577/1548-8659(2001)130<0878:FHRILG>2.0.CO;2 · DOI
  32. Özesmi, 1999, An artificial neural network approach to spatial habitat modeling with interspecific interaction, Ecol. Model., 116, 15, 10.1016/S0304-3800(98)00149-5 · DOI · Paper page
  33. Paruelo, 1997, Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models, Ecol. Model., 98, 173, 10.1016/S0304-3800(96)01913-8 · DOI · Paper page
  34. Ripley, 1994, Neural networks and related methods for classification, J. R. Stat. Soc. B, 56, 409, 10.1111/j.2517-6161.1994.tb01990.x · DOI · Paper page
  35. Scardi, 2001, Advances in neural network modeling of phytoplankton primary production, Ecol. Model., 146, 33, 10.1016/S0304-3800(01)00294-0 · DOI
  36. Scardi, 1999, Developing an empirical model of phytoplankton primary production: a neural network case study, Ecol. Model., 120, 213, 10.1016/S0304-3800(99)00103-9 · DOI · Paper page
  37. Schleiter, 1999, Modeling water quality, bioindication and population dynamics in lotic ecosystems using neural networks, Ecol. Model., 120, 271, 10.1016/S0304-3800(99)00108-8 · doi:10.1016/s0304-3800(99)00108-8
  38. Spitz, 1999, Environmental impact prediction using neural network modeling. An example in wildlife damage, J. Appl. Ecol., 36, 317, 10.1046/j.1365-2664.1999.00400.x · DOI
  39. White, 1989, Learning in artificial neural networks: a statistical perspective, Neural Computing, 1, 425, 10.1162/neco.1989.1.4.425 · DOI · Paper page

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Metadata

Authors
Olden, J. D., & Jackson, D. A.
Venue
Ecological Modelling
Publisher
Elsevier BV
Published
2002-08
Volume
154
Issue
1-2
Pages
135-150
ISSN
0304-3800 (ISSN-L)
DOI
10.1016/s0304-3800(02)00064-9
OpenAlex
W2106100979
Cited by
1,223
References
4
FWCI
9.03
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Funders
Natural Sciences and Engineering Research Council of Canada

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Olden, J. D., & Jackson, D. A. (2002). Illuminating the “black box”: a randomization approach for understanding variable contributions in artificial neural networks. Ecological Modelling. https://doi.org/10.1016/s0304-3800(02)00064-9

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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