Inference-Based Bayesian Network For Threatened Tree Species

Behailu Getachew*
Computing Department, Ethiopian Institute of Technology-Mekelle, Mekelle University, Mekelle, Ethiopia.
Periodicity:February - April'2011
DOI : https://doi.org/10.26634/jfet.6.3.1413

Abstract

In  the  recent  years,  Bayesian  Net  (BN)  is  an  increasingly  popular  formalism  for reasoning and  decision-making  in  problems  that   involve  uncertainty  and probabilistic reasoning. Bayesian Net gives the new advancements and innovations in Threatened  Tree Species   and   also   fulfilling   the   barriers   between   Knowledge Representation   and Artificial   intelligence.  The  species   degradation   calls   for  uncertainty  management  technique  that  aims  at  developing  knowledge  base  and  evaluating  the  status  of  the  species.  BN is a machine learning tool to provide an opportunity for predicting the species uncertainties in an Artificial intelligence system.

This paper reports a comparison of BN-based and subjective (elicit-based) prediction modeling and hence, relevant inference examples have been taken from the generated conditional  probability  table  (CPT)  so  as  to  define  decision  making  of  the  problem domain. The results demonstrate the performance prediction and inference BN model for   biological   variables   or   knowledge   base   via   Three-Phase-Dependency Analysis algorithm.

Keywords

Bayesian Net, Biological Variables, Threatened Tree Species, Prediction Modeling, Uncertainty

How to Cite this Article?

Getachew , B. (2011). Inference-Based Bayesian Network For Threatened Tree Species. i-manager’s Journal on Future Engineering and Technology, 6(3), 45-59. https://doi.org/10.26634/jfet.6.3.1413

References

[1]. Cain et al. (1999). Belief networks: a Frame work for the participatory development of natural resources management strategies.
[2]. Cheng, J. (1998). “Learning Bayesian Networks from Data: An Efficient Approach Based on Information Theory”, Technical Report, Department of Computer Science, University of Alberta.
[3]. Cooper, G.F., and Herskovits, E. (1992). A Bayesian Method for the induction of probabilistic networks from data. Machine Learning, 9 (pp. 309-347).
[4]. Finkel, (1996). An introduction to Bayesian inference for ecological research and environmental decisionmaking. Ecol. Appl. 6: 1036-1046.
[5]. Girma Balcha (2002). Conservation and sustainable use of Forest Genetic Resources. Addis Ababa, Ethiopia. PP. 150-157. In Proceedings of a National Conference on Forest Resources of Ethiopia.
[6]. Ham and Kamber (2001). Data mining: concepts and techniques.
[7]. IUCN (The World Conservation Union), (1994). IUCN Red List Categories. Gland: 21 PP.
[8]. Heckerman, D. (1995). A tutorial on learning Bayesian networks. Technical Report MSR-TR-95-06. Microsoft Research.
[9]. Jeffrey, R. C. (1992). Probability and the art of judgments. Cambridge University Press,Cambridge, UK.
[10]. Kanga and Kangas (2004). Probability, possibility and evidence: approaches to consider risk and uncertainty in forest decisions analysis.
[11]. Kuikka et al. (1999). Modeling environmentally driven uncertainties in Baltic cod Management by Bayesian influence diagrams.
[12]. Lauritzen, S. (1988). “Local computations with probabilities on graphical structures and their application to expert systems” in J. Royal Statistics Society B, 50(2), 157-194.
[13]. Marcot, et.al. (2006). Guidelines for Developing and Updating Bayesian belief network applied to ecological modeling and conservation, NRC, Canada..
[14]. McNay et al. (2006). A Bayesian Approach to evaluating habitat for woodland caribou in north-central British Columbia.
[15]. Mead et al. (2006). Applications of Bayesian networks in ecological modeling, Montana State University – Bozeman, USA.
[16]. Namkoong, gene and Koshy, Mathew P. (2000). Decision Making in gene Conservation. Forset genetic Resources 28
[17]. Neapolitan, R.E. (2004). “Learning Bayesian Networks”, Prentice Hall Series in Artificial Intelligence,
[18]. Newton, A., Oldfield, S., Fragoso, G., Mathew, P., Miles, L., & Edwards, M. (2003). Towards a Global Tree Conservation Atlas. UNEP-WCMC/FFI. http://www.unepwcmc. org/resources/ publications/treeatlas
[19]. Oldfield, S.F., Lusty, C., and MacKinven, A. (1998). The World List of Threatened Trees. World Conservation Press, Cambridge
[20]. Olson et al. (1990a). A framework for modeling uncertain reasoning in ecosystem management II: Bayesian belief network.
[21]. Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann.
[22]. Possingham, H. P. (1997). State-dependent decision analysis for conservation biology. Pages 298–304 in S. T. A. Pickett, R. S. Ostfield, M. Shachak, and G. E. Likens, editors. The ecological basis of conser vation: heterogeneity, ecosystems and biodiversity., Chapman and Hall, New York, New York, USA.
[23]. Rahel Bekele (2005). Computer-Assisted Learner Group Formation Based on Personality Traits, Hamburg, Germany.
[24]. Regan, H.M., M. Colyvan, and M.A. Burgman. (2002). A taxonomy and treatment of uncertainty for ecology and conser vation biology. Ecological Applications, 12:618–628.
[25]. Samir Abduselam (2001). An Application of Expert Systems on Species Selection: The case of Forestry Research Center.
[26]. Sando, T. (2005). Modeling Highway Crashes Using Bayesian Networks: The Florida State University, College of Engineering.
[27]. Schroth, G., et.al. (1996). Forest Ecology and Management, Volume 84, Issues 1-3, pp. 199- 208.
[28]. Spirtes, P. (2000). “Causation, Prediction and Search”, 2nd Edition, MIT Press,
[29]. Taye Bekele, Kumelachew Yeshitila, Shiferaw Dessie and Günther Haase (2002). Priority Woody Species of the Moist Montane Forests of Southwest Ethiopia: Consideration for Conservation.
[30]. Whitten, I.H., (2005). Data mining: Practical Machine Learning tools and techniques, Second Edition.
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