X
Xiaobin Xu
Researcher at Hangzhou Dianzi University
Publications - 80
Citations - 1034
Xiaobin Xu is an academic researcher from Hangzhou Dianzi University. The author has contributed to research in topics: Computer science & Evidential reasoning approach. The author has an hindex of 11, co-authored 53 publications receiving 657 citations. Previous affiliations of Xiaobin Xu include Tsinghua University.
Papers
More filters
Journal ArticleDOI
Data classification using evidence reasoning rule
TL;DR: The main aim of this paper is to present a classification method using a novel combination rule i.e., the evidence reasoning (ER) rule, an improvement of the DC rule, that defines the reliability and weight of evidence.
Journal ArticleDOI
Circuit Tolerance Design Using Belief Rule Base
TL;DR: The effectiveness of the proposed methodology is demonstrated through two typical numerical examples of the nonlinear performance functions with nonconvex and disconnected acceptability regions and high-dimensional input parameters and a real-world application in the parameter design of a track circuit for Chinese high-speed railway.
Journal ArticleDOI
Machine learning-based wear fault diagnosis for marine diesel engine by fusing multiple data-driven models
TL;DR: A novel method is presented to determine the reliability of evidence by considering the accuracy and stability of every single model, and the importance weight is optimized by genetic algorithm to improve the performance of the fusion system.
Journal ArticleDOI
The optimal design of industrial alarm systems based on evidence theory
TL;DR: Numerical experiments and an industrial case are given to show that the proposed procedure for the optimal design of industrial alarm systems based on evidence theory has a better performance than the classical design methods.
Journal ArticleDOI
Evidence reasoning rule-based classifier with uncertainty quantification
TL;DR: Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show the improved classifier can give higher classification accuracy than the original ER-based classifier without considering uncertainty quantification and other classical or mainstream classifiers.