Title: Sign fusion of multiple QPNs based on qualitative mutual information

Authors: Yali Lv; Jiye Liang; Yuhua Qian; Jiajie Wu; Suqin Ji

Addresses: Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China; School of Information Management, Shanxi University of Finance and Economics, Taiyuan 030006, China ' Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China ' Key Laboratory of Computational Intelligence and Chinese Information, Processing of Ministry of Education, Shanxi University, Taiyuan, 030006, China ' School of Information Management, Shanxi University of Finance and Economics, Taiyuan 030006, China ' School of Information Management, Shanxi University of Finance and Economics, Taiyuan 030006, China

Abstract: In the era of big data, the fusion of uncertain information from different data sources is a crucial issue in various applications. In this paper, a sign fusion method of multiple qualitative probabilistic networks (QPNs) with the same structure from different data sources is proposed. Specifically, firstly, the definition of parallel path in multiple QPNs is given and the problem of fusion ambiguity is described. Secondly, the fusion operator (⊕f-operator) theorem is introduced in detail, including its proof and algebraic properties. Further, an efficient sign fusion algorithm is proposed. Finally, experimental results demonstrate that our fusion algorithm is feasible and efficient.

Keywords: qualitative probabilistic reasoning; QPNs; Bayesian networks; sign fusion; qualitative mutual information.

DOI: 10.1504/IJCSE.2019.099650

International Journal of Computational Science and Engineering, 2019 Vol.19 No.1, pp.36 - 45

Received: 12 Aug 2016
Accepted: 03 Feb 2017

Published online: 02 May 2019 *

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