Evidential calibration of binary SVM classifiers

DOI10.1016/j.ijar.2015.05.002zbMath1352.68208OpenAlexW209020652MaRDI QIDQ274433

Philippe Xu, Franck Davoine, Hongbin Zha, Thierry Denoeux

Publication date: 22 April 2016

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2015.05.002

zbMATH Keywords

Dempster-Shafer theoryevidence theorysupport vector machineclassifier calibrationtheory of belief functions

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (8)

Prediction of future observations using belief functions: a likelihood-based approach ⋮ Face pixel detection using evidential calibration and fusion ⋮ SELP: semi-supervised evidential label propagation algorithm for graph data clustering ⋮ Uncertainty-Aware Parzen-Rosenblatt Classifier for Multiattribute Data ⋮ Active Evidential Calibration of Binary SVM Classifiers ⋮ Possibility measure based fuzzy support function machine for set-based fuzzy classifications ⋮ Evidential joint calibration of binary SVM classifiers ⋮ Decision-making with belief functions: a review

Uses Software

• LIBSVM
• AdaBoost.MH

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bagging predictors
• Generalized additive models
• The transferable belief model
• A decision-theoretic generalization of on-line learning and an application to boosting
• Classification by pairwise coupling
• A definition of subjective possibility
• The Dempster-Shafer calculus for statisticians
• Forecasting using belief functions: an application to marketing econometrics
• Machine learning in document analysis and recognition
• Constructing belief functions from sample data using multinomial confidence regions
• Likelihood-based belief function: justification and some extensions to low-quality data
• Rejoinder on ``Likelihood-based belief function: justification and some extensions to low-quality data
• An Empirical Distribution Function for Sampling with Incomplete Information
• Combining Pattern Classifiers
• An Introduction to Random Sets
• New Methods for Reasoning Towards Posterior Distributions Based on Sample Data
• Belief decision trees: Theoretical foundations

This page was built for publication: Evidential calibration of binary SVM classifiers