Abstract
The fuzzy Bayesian networks (FBNs) have gained substantial research interest in recent years. This paper presents a probabilistic performance analysis for NFBN, a new fuzzy Bayesian network learning approach, proposed in our earlier work. Previously, it has been shown empirically that NFBN produces encouraging results with high accuracy for multivariate time series prediction. The present paper focuses on the theoretical analysis of the learning performance of NFBN, based on four evaluation criteria, namely (1) consistency, (2) preciseness, (3) learning sensitivity to number of parents of any node in the network, and (4) learning sensitivity to domain size. Finally, using two case studies, it has been shown that the theoretical assessment conforms to the empirical results.
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Das, M., Ghosh, S.K. (2018). Performance Analysis for NFBN—A New Fuzzy Bayesian Network Learning Approach. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 708. Springer, Singapore. https://doi.org/10.1007/978-981-10-8636-6_38
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DOI: https://doi.org/10.1007/978-981-10-8636-6_38
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