Abstract
Bayesian Networks (BNs) have been recently employed to solve meteorology problems. In this paper, the application of BNs for mining a real-world weather dataset is described. The employed dataset discriminates between “wet fog” instances and “other weather conditions” instances, and it contains many missing data. Therefore, BNs were employed not only for classifying instances, but also for filling missing data. In addition, the Markov Blanket concept was employed to select relevant attributes. The efficacy of BNs to perform the aforementioned tasks was assessed by means of several experiments. In summary, more convincing results were obtained by taking advantage of the fact that BNs can directly (i.e. without data preparation) classify instances containing missing values. In addition, the attributes selected by means of the Markov Blanket provide a simpler, faster, and equally accurate classifier.
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Hruschka, E.R., Hruschka, E.R., Ebecken, N.F.F. (2006). Applying Bayesian Networks for Meteorological Data Mining. In: Macintosh, A., Ellis, R., Allen, T. (eds) Applications and Innovations in Intelligent Systems XIII. SGAI 2005. Springer, London. https://doi.org/10.1007/1-84628-224-1_10
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DOI: https://doi.org/10.1007/1-84628-224-1_10
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