| Summary: | Bayesian network has gained increasing popularity
among the data scientists and research communities, because of its
inherent capability of capturing probabilistic information and reasoning with uncertain knowledge. However, the discrete Bayesian
learning, with continuous and categorical variables, often shows
poor performance because of parameter value uncertainty, arising
due to strict boundary value of the discretized data and lack of
knowledge on domain semantics. In this work, we propose semFBnet, a variant of Bayesian network with incorporated fuzziness
and semantic knowledge, to reduce the uncertainty during parameter learning. The performance of semFBnet has been validated
with prediction of daily meteorological conditions in two states
of India, namely West Bengal and Delhi, for the years 2015 and
2016, respectively. The study of Dawid-Sebastiani score and the
confidence interval analysis, in comparison with the state-of-theart and benchmark prediction techniques, demonstrate the effectiveness of the proposed semFBnet in reducing parameter value
uncertainty.
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