Implementing Dempster's rule for hierarchial evidence
Artificial Intelligence
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the justification of Dempster's rule of combination
Artificial Intelligence
Original Contribution: Stacked generalization
Neural Networks
Combining the results of several neural network classifiers
Neural Networks
Machine Learning
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
MultiBoosting: A Technique for Combining Boosting and Wagging
Machine Learning
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Machine Learning
Evidence Theory and Its Applications
Evidence Theory and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Multiple Learning Strategies for Effective Cross Validation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On Combining Classifier Mass Functions for Text Categorization
IEEE Transactions on Knowledge and Data Engineering
Analyzing the degree of conflict among belief functions
Artificial Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Pairwise classifier combination using belief functions
Pattern Recognition Letters
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
Issues in stacked generalization
Journal of Artificial Intelligence Research
Constructing diverse classifier ensembles using artificial training examples
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An evidence-theoretic k-NN rule with parameter optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Application of majority voting to pattern recognition: an analysis of its behavior and performance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A neural network classifier based on Dempster-Shafer theory
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Learning from data with uncertain labels by boosting credal classifiers
Proceedings of the 1st ACM SIGKDD Workshop on Knowledge Discovery from Uncertain Data
A multidimensional hybrid intelligent method for gear fault diagnosis
Expert Systems with Applications: An International Journal
Mass function derivation and combination in multivariate data spaces
Information Sciences: an International Journal
Impact of multiple clusters on neural classification of ROIs in digital mammograms
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Two ensemble classifiers constructed from GEP-induced expression trees
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Classifier fusion in the Dempster--Shafer framework using optimized t-norm based combination rules
International Journal of Approximate Reasoning
Cellular GEP-induced classifiers
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Maximal confidence intervals of the interval-valued belief structure and applications
Information Sciences: an International Journal
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
Analyzing the relationship between diversity and evidential fusion accuracy
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Cellular gene expression programming classifier learning
Transactions on computational collective intelligence V
Quality-aware similarity assessment for entity matching in Web data
Information Systems
The impact of diversity on the accuracy of evidential classifier ensembles
International Journal of Approximate Reasoning
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In many domains when we have several competing classifiers available we want to synthesize them or some of them to get a more accurate classifier by a combination function. In this paper we propose a 'class-indifferent' method for combining classifier decisions represented by evidential structures called triplet and quartet, using Dempster's rule of combination. This method is unique in that it distinguishes important elements from the trivial ones in representing classifier decisions, makes use of more information than others in calculating the support for class labels and provides a practical way to apply the theoretically appealing Dempster-Shafer theory of evidence to the problem of ensemble learning. We present a formalism for modelling classifier decisions as triplet mass functions and we establish a range of formulae for combining these mass functions in order to arrive at a consensus decision. In addition we carry out a comparative study with the alternatives of simplet and dichotomous structure and also compare two combination methods, Dempster's rule and majority voting, over the UCI benchmark data, to demonstrate the advantage our approach offers.