Intelligent Data Analysis for Conservation: Experiments with Rhino Horn Fingerprint Identification
Conservation is an area in which a great deal of data has been collected over many years. Intelligent Data Analysis offers the possibility of analysing this data in an automatic fashion to map characteristics, identify trends and offer guidance for conservation action. This paper is concerned with the use of techniques of Intelligent Data Analysis for an important task in animal conservation: the identification of the species and origin of illegally traded or confiscated African rhino horn. It builds on an earlier analysis by the African Rhino Specialist Group. It is demonstrated that it is possible to distinguish between both species and country of origin with a high degree of accuracy and that the results are also likely to be suitable for use in court.
KeywordsPredictive Accuracy Confusion Matrix Probabilistic Neural Network Discriminant Function Analysis Intelligent Data Analysis
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- Emslie, R.H. and Brooks, P.M. (1999). African Rhino. Status Survey and Conservation Action Plan IUCN/SSC African Rhino Specialist Group. IUCN, Gland, Switzerland and Cambridge, UK.Google Scholar
- Emslie, R.H., Brooks, P.M., Lee-Thorp, J.A., Jolles, A., Smith, W. and Vermaas, N. (2001). Development of a Continental African Rhino Horn Fingerprinting Database and Statistical Models to Determine the Probable Species and Source of Rhino Horn. AfRSG Rhino Horn Fingerprinting for Security Project 9F0084.1. Unpublished Report to WWFGoogle Scholar
- Lee-Thorp, J.A., van der Merwe N.J. and Armstrong R.A. (1992). Final Project Report ZA309: Source Area Determination of Rhino Horn by Isotropic Analysis. Unpublished WWF ReportGoogle Scholar
- Hall-Martin, A.J., van der Merwe N.J., Lee-Thorp J.A., Armstrong R.A., Mehl, C.H., Struben, S. and Tykot, R. (1993). Determination of Species and Geographic Origin of Rhinoceros by Isotropic Analysis and its Possible Implication to Trade Controls. Proceedings of International Rhino Conference, San Diego, California, p. 123–124Google Scholar
- Hart, R.J., Tredoux, M. and Damarupurshad, A. (1994). The Characterisation of Rhino Horn and Elephant Ivory Using the Technique of Neuron Activation Analysis. Final report on a project undertaken on behalf of the Department of Environmental Affairs, South AfricaGoogle Scholar
- Masters, T. (1993). Practical Neural Network Recipes in C++, Academic PressGoogle Scholar
- Hunt, E.B., Marin J. and Stone, P.J. (1966). Experiments in Induction. Academic PressGoogle Scholar
- Quinlan, J.R. (1986). Induction of Decision Trees. Machine Learning, 1: 81–106Google Scholar
- Quinlan, J.R. (1993). C4.5: Programs for Machine Learning. Morgan KaufmannGoogle Scholar
- Bramer, M.A. (2000). Automatic Induction of Classification Rules from Examples Using N-Prism. In: Research and Development in Intelligent Systems XVI. Springer-Verlag, pp. 99–121Google Scholar
- Bramer, M.A. (2000). Inducer: a Rule Induction Workbench for Data Mining. In Proceedings of the 16th IFIP World Computer Congress Conference on Intelligent Information Processing (eds. Z. Shi, B. Faltings and M. Musen). Publishing House of Electronics Industry (Beijing), pp. 499–506Google Scholar