Modeling Hesitation and Conflict: A Belief-Based Approach for Multi-class Problems

Authors:
Thomas Burger;Oya Aran;Alice Caplier
Affiliations:
France Telecom R&D, France;Bogazici University, Turkey;INPG-LIS, France
Venue:
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Year:
2006

Citing 0
Cited 4

A belief-based sequential fusion approach for fusing manual signs and non-manual signals

Pattern Recognition
Sequential Belief-Based Fusion of Manual and Non-manual Information for Recognizing Isolated Signs

Gesture-Based Human-Computer Interaction and Simulation
Combining the results in pairwise classification using dempster-shafer theory: a comparison of two approaches

ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
Constructing dynamic frames of discernment in cases of large number of classes

ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support Vector Machine (SVM) is a powerful tool for binary classification. Numerous methods are known to fuse several binary SVMs into multi-class (MC) classifiers. These methods are efficient, but an accurate study of the misclassified items leads to notice two sources of mistakes: (1) the response of each classifier does not use the entire information from the SVM, and (2) the decision method does not use the entire information from the classifier responses. In this paper, we present a method which partially prevents these two losses of information by applying Belief Theories (BTs) to SVM fusion, while keeping the efficient aspect of the classical methods.