Skip to main content

Classification Schemes for Priority Setting and Decision Making

A Selected Review of Expert Judgment, Rule-Based, And Prototype Methods

  • Conference paper
Comparative Risk Assessment and Environmental Decision Making

Part of the book series: Nato Science Series: IV: Earth and Environmental Sciences ((NAIV,volume 38))

Abstract

Agencies and organizations charged with priority setting require analytical approaches that are accurate, efficient, and reliable. Increasingly, decision analysis is applied using formal techniques that are measurable and repeatable. This paper surveys available methods ranging from expert judgment approaches to complex statistical models, and considers the benefits and issues raised for decision making that applies various approaches.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baxt, W.G. 1991. Use of an Artificial Neural Network for the Diagnosis of Myocardial Infarction. Annals of Internal Medicine, 115, 843–848: 1991.

    CAS  Google Scholar 

  2. Breiman, L; Friedman, J.H.; Olshen, R.; Stone, C.J., 1984. Classification and Regression Trees. Wadsworth International Group, Belmont, CA.

    Google Scholar 

  3. Commission of European Communities. 1997. European Union System for the Evaluation of Substances. EUSES 1.0 User Manual.

    Google Scholar 

  4. Commission on Geosciences, Environment, and Resources. 1998. Setting Priorities for Drinking Water Contaminants. National Academy Press. P. 113.

    Google Scholar 

  5. Davis, G.A., M. Swanson, and S. Jones. 1994. Comparative Evaluation of Chemical Ranking and Scoring Methodologies. Prepared for U.S. Environmental Protection Agency, Office of Pollution Prevention and Toxics, Washington, DC http://eerc.ra.utk.edu/clean/pdfs/CECRSM.pdf.

  6. Fielding, A. 2000a. Biological Data Processing II: Multivariate Techniques. http://149.170.199.144/multivar/intro.htm#Multivariate

  7. Fielding, A. 2000b. Joining Clusters: Clustering Algorithms. (http://149.170.199.144/multivar/ca_alg.htm)

  8. Flug, M., H.L.H. Seitz, and J.F. Scott. 2000. Multicriteria Decision Analysis Applied to Glen Canyon Dam. Journal of Water Resources Planning and Management, ASCE, Vol. 126: 270–276.

    Google Scholar 

  9. Freeman, K. 2000. “Psychic networks: training computers to predict algal blooms.” Environmental Health Perspectives; Oct 108(10): A464–7.

    Google Scholar 

  10. Fuzzy Logic and Fuzzy Expert Systems Newsgroup. http://www-2.cs.cmu.edu/Groups/AI/html/faqs/ai/fuzzy/part1/faq.html

  11. Fuzzy Logic in Environmental Sciences: A Bibliography. http://www.bjarne.ca/fuzzy_environment/#refs

  12. Goossens, L.H.J., and R.M. Cooke. 2001. Expert Judgment Elicitation in Risk Assessment, In: I. Linkov and J. Palma-Oliveira (eds.), Assessment and Management of Environmental Risks, 411–426. NATO Science Series IV. Earth and Environmental Sciences Vol. 4. Kluwer Academic Publishers. Dordrecht.

    Google Scholar 

  13. Grassi M.; Villani S.; Marinoni A. Classification methods for the identification of ‘case’ in epidemiological diagnosis of asthma. European Journal of Epidemiology, 2001,vol. 17,no. 1, pp. 19–29(11)

    Article  CAS  Google Scholar 

  14. Green, P.J. and B.W. Silverman. 1994. Nonparametric Regression and Generalized Linear Models A Roughness Penalty Approach. Chapman and Hall, London, U.K.

    Google Scholar 

  15. Harmonized Integrated Classification System for Human Health and Environmental Hazards of Chemical Substances and Mixtures. OECD Series on Testing and Assessment No. 33

    Google Scholar 

  16. Hastie, T. 1996. Computation: Neural Networks. In: J. Wiley (ed), Encyclopedia of Biostatistics.

    Google Scholar 

  17. Hastie, T. R. Tibshirani, and A. Buja. Flexible Discriminant and Mixture Models. Chapter 1 in, Statistics and Neural Networks — Advances at the Interface J. W. Kay, and D. M. Titterington 2000. Oxford University Press.

    Google Scholar 

  18. Hastie, T., Tibshirani, R., and Friedman, J., 2001. The Elements of Statistical Learning. Springer-Verlag, New York.

    Google Scholar 

  19. Hastie, T.J. and Tibshirani, R.J., 1990. Generalized Additive Models. Chapman and Hall, London.

    Google Scholar 

  20. Hastie, T.J. and Tibshirani, R.J., 1996. Discriminant analysis by Gaussian mixtures, Journal of Royal Statistical Society, B. 58:155–176.

    Google Scholar 

  21. Hastie, T.J., Buja, A., and Tibshirani, R.J., 1995. Penalized discriminant analysis. Annals of Statistics, 23:73–102.

    Google Scholar 

  22. Hastie, T.J., Tibshirani, R.J., and Buja, A., 1994. Flexible discriminant analysis by optimal scoring. Journal of American Statistical Association. 89:1255–1270.

    Google Scholar 

  23. Heckerman, D. 1995 A Tutorial on Learning With Bayesian Networks. Microsoft Research. MSR-TR-95-06

    Google Scholar 

  24. Heller, M., and Q. Wang, 1996. “Improving Potable Water Demand Forecasts with Neural Networks,” in Proceedings of UCOWR 1996, San Antonio, TX. New Waves Volume 9: 2.

    Google Scholar 

  25. Hinton, G.E. 1992. How Neural Networks Learn from Experience. Scientific American, September, 1992: 145–151.

    Google Scholar 

  26. http://www.epa.gov/oppfead1/harmonization/docs/doc/integr~1.doc.

  27. http://www.epa.gov/opptintr/exposure/docs/srd.htm

  28. http://smig.usgs.gov/SMIG/nnmodel_refs.htmlhttp://wtvw-stat.stanford.edu/~hastie/Papers/

  29. Huuskonen, J., 2001: Estimation of water solubility from atom-type electrotopological state indices. Environ. Toxicol. Chem. 20, 491–497.

    CAS  Google Scholar 

  30. Huuskonen, J., 2000 Livingstone, D.J. & Tetko, I.V.: Neural network modeling for estimation of partition coefficient based on atom-type electrotopological state indeces, J. Chem. Inf. Comput. Sci. 40, 947–955.

    CAS  Google Scholar 

  31. Inductive Solutions, Inc. 2001. Neural Network and NNet Sheet FAQ http://www.inductive.com/softnet.htm

  32. Janssen, R. 2001. On the use of multi-criteria analysis in environmental impact assessment in The Netherlands. Journal of Multi-Criteria Decision Analysis, vol. 10: 101–109.

    Article  Google Scholar 

  33. Kerr, M. 2001. The Delphi Process, http://www.rararibids.org.uk/documents/bid79-delphi.htm

  34. Kilsson, N.J. 1996. Introduction to Machine Learning. http://robotics.stanford.edu/people/nilsson/mlbook.html

  35. Kon, M.A., and L. Plaskoa. 1997. Neural Networks, Radial Basis Functions, and Complexity. Proceedings of Bialowieza Conference on Statistical Physics, 122–145. http://math.bu.edu/people/mkon/nnpap3.pdf

  36. Lin, H. and S. Wang. 2001. GIS Supported Modeling of Water Quality Using Artificial Neural Network (ANN) in the Tomorrow/Waupaca River Watershed. http://www.uwsp.edu/water/portage/action/sheng.htm

  37. Lootsma, F. A. 2000. The decision analysis and support project. Journal of Multi-Criteria Decision Analysis, vol. 9: 7–10.

    Google Scholar 

  38. Michie, D., D.J. Spiegelhalter, and C.C. Taylor (eds). 1994. Machine Learning, Neural and Statistical Classification. Ellis Horwood. http://www.amsta.leeds.ac.uk/~charles/statlog/

  39. Nerini D.; Durbec J.P.; Mante C.; Garcia F.; Ghattas B. Forecasting Physicochemical Variables by a Classification Tree Method. Application to the Berre Lagoon (South France). Acta Biotheoretica, December 2000, vol. 48, no. 3/4, pp. 181–196(16).

    Article  CAS  Google Scholar 

  40. Neusciences. 2001. Support Vector Machines. http://www.neusciences.com/Technologies/collaboration_nats.htm

  41. Nighswonger, G. 2000. ANNs Provide Tools for Increased Diagnostic Accuracy Medical Device and Diagnostic Industry. January Edition.

    Google Scholar 

  42. NRC. 1999a. Setting Priorities for Drinking Water Contaminants. National Academy Press.

    Google Scholar 

  43. NRC. 1999b. Identifying Future Drinking Water Contaminants. National Academy Press.

    Google Scholar 

  44. NRC. 2001. Classifying Drinking Water Contaminants for Regulatory Consideration. National Academy Press.

    Google Scholar 

  45. Organisation for Economic Co-operation and Development. 2001. Joint Meeting of the Chemicals Committee and the Working Party on Chemicals, Pesticides and Biotechnology. http://www1.oecd.org/ehs/Class/hclfinaw.pdf

  46. Pontil, M, R. Rifkin, and T. Evgeniou. 1998. From Regression to Classification in Support Vector Machines. Massachusetts Institute of Technology, AI Memo 1649. ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1649.pdf

  47. Pontil, M. and A. Verri. 1997. Properties of Support Vector Machines. Massachusetts Institute of Technology, AI Memo 1612. ftp://publications.ai.mit.edu/ai-publications/pdf/AIM-1612.pdf

  48. Qian, S. and C. W. Anderson. Exploring Factors Controlling the Variability of Pesticide Concentrations in the Willamette River Basin Using Tree-Based Models. Environ. Sci. Technol. 1999. 33, 3332–3340.

    Article  CAS  Google Scholar 

  49. Qian, S., W. Warren-Hicks, J. Keating, D.R.J. Moore and R. S Teed. 2000. A Predictive Model of Mercury Fish Tissue Concentrations for the Southeastern United States. Environ. Sci. Technol. 35(5):941–947.

    Google Scholar 

  50. R. Brause, T. Langsdorf, M. Hepp. Credit Card Fraud Detection by Adaptive Neural Data Mining. Internal Report 7/99, FB Informatik, University of Frankfurta.M., 1999.

    Google Scholar 

  51. R-F Yu, R.F., S.F Kang, S-L Liaw and M-c Chen. 2000. Application of artificial neural network to control the coagulant dosing in water treatment plant. Water Science & Technology Vol 42 No 3–4 pp 403.

    CAS  Google Scholar 

  52. Ripley, B.D., 1996. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, UK

    Google Scholar 

  53. S. Lawrence, C. L. Giles, A. Tsoi, and A. Back, January 1997, “Face recognition: A convolutional neural-network approach,” IEEE Trans, on Neural Networks, vol. 8, pp. 98–113.

    Google Scholar 

  54. Sarle, W. 2001. FAQ’s on Neural Networks. ftp://ftp.sas.com/pub/neural/FAQ.html#questions

  55. Sarle, W.S. 1994. Neural Networks and Statistical Models. Proceedings of the Nineteenth Annual SAS Users Group International Conference, April, 1994.

    Google Scholar 

  56. Smith, L. 2001. An Introduction to Neural Networks, http://www.cs.stir.ac.uk/~lss

  57. SRA. 2001. P.A. Murphy, G.E. Rice, USEPA. Overview of Comparative Risk-Integration of Scientific Ideas and Approaches, Society for Risk Analysis Annual Meeting December 5, 2001. Seattle, WA.

    Google Scholar 

  58. StatSoft. 2002. Statistica Neural Networks. http://www.statsoftinc.com/textbook/stneunet.html#intro

  59. Shatkin, J.A. and J.M. Palma-Oliviera, C.A. Patton, C. Saraiva. 1998. Comparative Risk Assessment Method to Evaluate Impacts of Portuguese Industrial Waste Disposal. Society for Risk Annual Meeting and Expositon.

    Google Scholar 

  60. Stiber, NA., M. Pantazidou, and M.J. Small. 1999. Expert system methodology for evaluating reductive dechlorination at TCE sites. Environ. Sci. Technol. 33:3012–3020.

    Article  CAS  Google Scholar 

  61. Stow, C.A. and Borsuk, M.E., 2002. Enhancing causal assessment of estuarine fishkills using graphical models. To appear in Ecosystems.

    Google Scholar 

  62. The Cadmus Group, Inc. 1992. The Cadmus Risk Index Approach.

    Google Scholar 

  63. The Consummate Design Center. 1996. The Delphi Process. http://www.tcdc.com/dmeths/dmeth5b.htm

  64. Turoff, M. and S.R. Hiltz. 1996. Computer Based Delphi Processes. http://eies.njit.edu/~turoff/Papers/delphi3.html

  65. U.S. Environmental Protection Agency. 1994. Waste Minimization Prioritization Tool Beta Test Version 1.0: User’s Guide and System Document.

    Google Scholar 

  66. US Environmental Protection Agency (US EPA). 1994. Chemical Hazard Evaluation Management Strategies: A Method for Ranking and Scoring Chemicals by Potential Human Health and Environmental Impacts, http://www.epa.gov/opptintr/cgi-bin/claritgw

  67. US EPA. 1997. Announcement of the Draft Drinking Water Candidate Contaminant List; Notice. 62 FR 52194.

    Google Scholar 

  68. US EPA. 1998. Agency Guidance for Conducting External Peer Review of Environmental Regulatory Modeling http://www.epa.gov/ospinter/spc/modelpr.htm

  69. US EPA. 2001a. Screening Level Tools, http://www.epa.gov/opptintr/exposure/docs/screen.htm

  70. US EPA. 2001b. Source Ranking Database (SRD).

    Google Scholar 

  71. US EPA. 2001c. Use Clusters Scoring System.

    Google Scholar 

  72. Wei, B., N. Sugiura, and T. Maekawa. 2001. “Use of artificial neural network in the prediction of algal blooms.” Water Research. Jun; 35(8): 2022–8.

    Article  CAS  Google Scholar 

  73. Weisman, O., and Z. Pollack. 1995. The Perceptron. http://www.cs.bgu.ac.il/~omri/Perceptron/

  74. Wenstop, F., and K. Seip. 2001. Legitimacy and quality of multi-criteria environmental policy analysis: a meta-analysis of five MCE studies in Norway. Journal of Multi-Criteria Decision Analysis, vol. 10:53–64.

    Google Scholar 

  75. Wilson, R.A., and F. Keil. 2001. Decision Trees. The MIT Encyclopedia of the Cognitive Sciences. http://cognet.mit.edu/MITECS/Entry/utgoff.html

  76. Woo, Y., D. Lai, J.L. McLain, M.K. Manibusan, and V. Dellarco. 2002. Use of Mechanism-Based Structure-Activity Relationships Analysis in Carcinogenic Potential Ranking for Drinking Water Disinfection By-Products. Environmental Health Perspectives, Vol. 110:75–87.

    CAS  Google Scholar 

  77. Z Solutions. 1999. A Light Introduction to Neural Networks, http://zsolutions.com/light.htm

  78. Zaknixh, A. 1998. Artificial Neural Networks: An introductory course. http://www.maths.uwa.edu.au/~rkealley/ann_all/

  79. Zhu, J. and Hastie, T., 2001. “Kernel Logistic Regression and the Import Vector Machine”, refereed paper accepted for NIPS2001 conference, Vancouver, November 2001.

    Google Scholar 

  80. Neelakantan, T., Brion, G.M., and Lingireddy, S., 2001, Neural network modeling of cryptosporidium and giardia concentrations in the Delaware River, Water Science and Technology, 43(12), 125–132.

    CAS  Google Scholar 

  81. Tain, Y-1, T. Kanade and J.F. Cohn. 2001. Recognizing Action Units for Facial Expression Analysis. Transactions on Pattern Analysis and Machine Intelligence. 23:2, 97–115.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2004 Kluwer Academic Publishers

About this paper

Cite this paper

Shatkin, J.A., Qian, S. (2004). Classification Schemes for Priority Setting and Decision Making. In: Linkov, I., Ramadan, A.B. (eds) Comparative Risk Assessment and Environmental Decision Making. Nato Science Series: IV: Earth and Environmental Sciences, vol 38. Springer, Dordrecht. https://doi.org/10.1007/1-4020-2243-3_13

Download citation

Publish with us

Policies and ethics