Novelty detection: a review—part 2: neural network based approaches
Signal Processing
An evidential approach in ensembles
Proceedings of the 2006 ACM symposium on Applied computing
Evaluation for uncertain image classification and segmentation
Pattern Recognition
On the combination and normalization of interval-valued belief structures
Information Sciences: an International Journal
Pairwise classifier combination using belief functions
Pattern Recognition Letters
Evidential reasoning approach for bridge condition assessment
Expert Systems with Applications: An International Journal
The Journal of Supercomputing
A novel conflict reassignment method based on grey relational analysis (GRA)
Pattern Recognition Letters
Dual Antenna Receivers for High Data Rate Terminals
Wireless Personal Communications: An International Journal
Combining rough decisions for intelligent text mining using Dempster's rule
Artificial Intelligence Review
Decision trees as possibilistic classifiers
International Journal of Approximate Reasoning
COMBINING MULTIPLE CLASSIFIERS USING DEMPSTER'S RULE FOR TEXT CATEGORIZATION
Applied Artificial Intelligence
The combination of multiple classifiers using an evidential reasoning approach
Artificial Intelligence
Conditional Dempster-Shafer Theory for Uncertain Knowledge Updating
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
International Journal of Approximate Reasoning
On combining multiple classifiers using an evidential approach
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
A new technique for combining multiple classifiers using the dempster-shafer theory of evidence
Journal of Artificial Intelligence Research
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
A novel bit-level DS combining scheme for MIMO systems with HARQ
ISIT'09 Proceedings of the 2009 IEEE international conference on Symposium on Information Theory - Volume 1
Hierarchical and conditional combination of belief functions induced by visual tracking
International Journal of Approximate Reasoning
Imperfect pattern recognition using the fuzzy measure theory
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Fusion of possibilistic sources of evidences for pattern recognition
Integrated Computer-Aided Engineering
Information imperfection processing in supervised classification systems
AIKED'10 Proceedings of the 9th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Uncertainty processing applied to packet combining in MIMO-HARQ systems
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Towards a possibilistic classification of gastroenterology patterns in a complex environment
WSEAS TRANSACTIONS on SYSTEMS
Combining neural networks based on Dempster-Shafer theory for classifying data with imperfect labels
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
On the dynamic evidential reasoning algorithm for fault prediction
Expert Systems with Applications: An International Journal
A belief function classifier based on information provided by noisy and dependent features
International Journal of Approximate Reasoning
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
Maximal confidence intervals of the interval-valued belief structure and applications
Information Sciences: an International Journal
A family of measures for best top-n class-selective decision rules
Pattern Recognition
A Novel DST-based Packet Combining Scheme for MIMO-HARQ Systems
Wireless Personal Communications: An International Journal
Expert Systems with Applications: An International Journal
A novel approach for meeting the challenges of the integrated security systems
WSEAS TRANSACTIONS on SYSTEMS
Classification systems based on rough sets under the belief function framework
International Journal of Approximate Reasoning
MMM'07 Proceedings of the 13th International conference on Multimedia Modeling - Volume Part II
Autonomous and deterministic clustering for evidence-theoretic classifier
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
An effective combination of multiple classifiers for toxicity prediction
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Modeling and characterization of plasma processes using modular neural network
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Distances in evidence theory: Comprehensive survey and generalizations
International Journal of Approximate Reasoning
Using dempster-shafer theory in MCF systems to reject samples
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Combining uncertainty and imprecision in models of medical diagnosis
Information Sciences: an International Journal
Information Sciences: an International Journal
A skin detection approach based on the Dempster--Shafer theory of evidence
International Journal of Approximate Reasoning
Cellular automata based on artificial neural network for simulating land use changes
Proceedings of the 45th Annual Simulation Symposium
Random subspace evidence classifier
Neurocomputing
Generic discounting evaluation approach for urban image classification
IUKM'13 Proceedings of the 2013 international conference on Integrated Uncertainty in Knowledge Modelling and Decision Making
A belief classification rule for imprecise data
Applied Intelligence
Robust human action recognition scheme based on high-level feature fusion
Multimedia Tools and Applications
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A new adaptive pattern classifier based on the Dempster-Shafer theory of evidence is presented. This method uses reference patterns as items of evidence regarding the class membership of each input pattern under consideration. This evidence is represented by basic belief assignments (BBA) and pooled using the Dempster's rule of combination. This procedure can be implemented in a multilayer neural network with specific architecture consisting of one input layer, two hidden layers and one output layer. The weight vector, the receptive field and the class membership of each prototype are determined by minimizing the mean squared differences between the classifier outputs and target values. After training, the classifier computes for each input vector a BBA that provides a description of the uncertainty pertaining to the class of the current pattern, given the available evidence. This information may be used to implement various decision rules allowing for ambiguous pattern rejection and novelty detection. The outputs of several classifiers may also be combined in a sensor fusion context, yielding decision procedures which are very robust to sensor failures or changes in the system environment. Experiments with simulated and real data demonstrate the excellent performance of this classification scheme as compared to existing statistical and neural network techniques