A novel nonlinear causal inference approach using vector‐based belief rule base. Issue 9 (3rd June 2021)

• Record Type: Journal Article
• Authors: Zhenjie, Zhang; Xiaobin, Xu; Peng, Chen; Xudong, Wu; Xiaojian, Xu; Guodong, Wang; Schahram, Dustdar
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 9(2021)
• Page Start: 5005
• Page End: 5027
• Publication Date: 2021-06-03
• Subjects: attribute vector matching -- evidential reasoning -- full activation -- nonlinear causal inference -- vector‐based belief rule base; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22500
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: When using the belief rule base (BRB) methodology to deal with the nonlinear causal inference problems, combinatorial explosion often occurs due to overnumbered antecedent attributes, resulting in poor performance. Therefore, this paper proposes a novel nonlinear causal inference approach based on vector‐based BRB. In the modeling process of BRB, the original attributes are ranked by contribution rate and transformed into attribute vectors. Meanwhile, combined with the k ‐means method, appropriate referential vectors are obtained. Thereby a vector‐based BRB can be established. In the inference process of BRB, the idea of full activation of vector‐based rules is presented. By calculating the spatial matching degree of the testing sample and the referential vectors, activation weights of the rules which are used in the evidential reasoning algorithm are acquired. Experimental results of a nonlinear function with four‐dimensional input and the pipeline leakage detection data show the effectiveness and superiority of the proposed approach.