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Discretizing environmental data for learning Bayesian-network classifiers

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  • Ropero, R.F.
  • Renooij, S.
  • van der Gaag, L.C.

Abstract

For predicting the presence of different bird species in Andalusia from land-use data, we compare the performances of Bayesian-network classifiers and logistic-regression models. In our study, both well balanced and less balanced data sets are used, and models are learned from both the original continuous data and from the data after discretization. For the latter purpose, four different discretization methods, called Equal Frequency, Equal Width, Chi-Merge and MDLP, are compared. The experimental results from our species data sets suggest that the simple Naive Bayesian classifiers are preferable to logistic-regression models and that the relatively unknown Chi-Merge method is the preferred method for discretizing these environmental data.

Suggested Citation

  • Ropero, R.F. & Renooij, S. & van der Gaag, L.C., 2018. "Discretizing environmental data for learning Bayesian-network classifiers," Ecological Modelling, Elsevier, vol. 368(C), pages 391-403.
    • Handle: RePEc:eee:ecomod:v:368:y:2018:i:c:p:391-403
    DOI: 10.1016/j.ecolmodel.2017.12.015
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    References listed on IDEAS

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    1. Pollino, Carmel A. & White, Andrea K. & Hart, Barry T., 2007. "Examination of conflicts and improved strategies for the management of an endangered Eucalypt species using Bayesian networks," Ecological Modelling, Elsevier, vol. 201(1), pages 37-59.
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    3. Rafael Rumí & Antonio Salmerón & Serafín Moral, 2006. "Estimating mixtures of truncated exponentials in hybrid bayesian networks," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(2), pages 397-421, September.
    4. Ropero, R.F. & Aguilera, P.A. & Rumí, R., 2015. "Analysis of the socioecological structure and dynamics of the territory using a hybrid Bayesian network classifier," Ecological Modelling, Elsevier, vol. 311(C), pages 73-87.
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    Cited by:

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    4. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 0. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 0, pages 1-22.
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