The combination of multiple classifiers using an evidential reasoning approach

DOI10.1016/j.artint.2008.06.002zbMath1184.68385OpenAlexW2029169022MaRDI QIDQ2389681

Yaxin Bi, David. A. Bell, J. W. Guan

Publication date: 17 July 2009

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.artint.2008.06.002

zbMATH Keywords

evidential reasoningensemble methodscombination functionsDempster's rule of combinationevidential structures

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Measuring Impact of Diversity of Classifiers on the Accuracy of Evidential Ensemble Classifiers ⋮ HOW TO RANDOMLY GENERATE MASS FUNCTIONS ⋮ Two-probabilities focused combination in recommender systems ⋮ Approximate classification with web ontologies through evidential terminological trees and forests ⋮ Logistic Regression Revisited: Belief Function Analysis ⋮ Matrix games with payoffs of belief structures ⋮ Heterogeneous information fusion: combination of multiple supervised and unsupervised classification methods based on belief functions

Uses Software

• UCI-ml

Cites Work

• Bagging predictors
• Reinvestigating Dempster's idea on evidence combination
• Is combining classifiers with stacking better than selecting the best one?
• Implementing Dempster's rule for hierarchical evidence
• Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
• Approximating the combination of belief functions using the fast Möbius transform in a coarsened frame.
• On the justification of Dempster's rule of combination
• Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence
• Analyzing the degree of conflict among belief functions
• Upper and Lower Probabilities Induced by a Multivalued Mapping
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