Learning qualitative probabilistic networks with reduced ambiguous signs from complete data Online publication date: Tue, 28-Nov-2023
by Qian Wang; Wei Xu; Yali Lv; Lifang Wang
International Journal of Computing Science and Mathematics (IJCSM), Vol. 18, No. 4, 2023
Abstract: Qualitative probabilistic networks (QPNs) are the qualitative abstractions of probabilistic networks or Bayesian networks, summarising probabilistic influences by qualitative signs. But the ambiguous sign is undesirable as it leads to uninformative results upon QPN inference. In this paper, we focus on learning QPN with reduced ambiguous signs from complete data. We first extend the definition of qualitative influences based on the situational signs, and prove the theorem on the range of qualitative signs. Second, we design an algorithm for learning QPN with reduced ambiguous (RAQPN Algorithm). The QPN structure can be optimised by the K2 search algorithm, and the corresponding qualitative signs can be acquired by ordering the conditional probabilities that are represented by the frequency formats. Furthermore, when the ambiguous signs appear, they can be reduced by being given the situational state of the network. Finally, experiment results verify the validity and feasibility of the proposed methods.
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