Determination of evidence correction factors based on the neural network. Issue 2 (14th February 2017)

• Record Type: Journal Article
• Authors: Zhu, Weidong; Xu, Youhua; Wu, Yong; Sun, Yibo
• Other Names: Fernández‐Caballero Antonio guestEditor.; González Pascual guestEditor.; Navarro Elena guestEditor.
• Is Part Of: Expert systems. Volume 34:Issue 2(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-02-14
• Subjects: Dempster's rule of combination -- D–S evidence theory -- evidence correction factors -- neural network; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12192
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: A modified method to combine evidence based on artificial neural network and Dempster's rule of combination is proposed. The comprehensive discounting factor of this method adds learning and data mining capability to evidence combination, making it more suitable for the interrelated, conflicted evidence, or evidence with different importance and more suitable for group decision making. This method can be used for repeatable and verifiable systems. A study about securities market experts group prediction is conducted to verify the effectiveness of the proposed method.