A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Representation, independence, and combination of evidence in the Dempster-Shafer theory
Advances in the Dempster-Shafer theory of evidence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Combination of Text Classifiers Using Reliability Indicators
Information Retrieval
An analysis of diversity measures
Machine Learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Analyzing the combination of conflicting belief functions
Information Fusion
An efficient triplet-based algorithm for evidential reasoning
International Journal of Intelligent Systems
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
Analyzing the degree of conflict among belief functions
Artificial Intelligence
International Journal of Approximate Reasoning
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
“Good” and “bad” diversity in majority vote ensembles
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Multi-information ensemble diversity
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
Adaptive splitting and selection method for noninvasive recognition of liver fibrosis stage
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part II
A survey of multiple classifier systems as hybrid systems
Information Fusion
Diversity measures for one-class classifier ensembles
Neurocomputing
Clustering-based ensembles for one-class classification
Information Sciences: an International Journal
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Diversity being inherent in classifiers is widely acknowledged as an important issue in constructing successful classifier ensembles. Although many statistics have been employed in measuring diversity among classifiers to ascertain whether it correlates with ensemble performance in the literature, most of these measures are incorporated and explained in a non-evidential context. In this paper, we provide a modelling for formulating classifier outputs as triplet mass functions and a uniform notation for defining diversity measures. We then assess the relationship between diversity obtained by four pairwise and non-pairwise diversity measures and the improvement in accuracy of classifiers combined in different orders by Demspter's rule of combination, Smets' conjunctive rule, the Proportion and Yager's rules in the framework of belief functions. Our experimental results demonstrate that the accuracy of classifiers combined by Dempster's rule is not strongly correlated with the diversity obtained by the four measures, and the correlation between the diversity and the ensemble accuracy made by Proportion and Yager's rules is negative, which is not in favor of the claim that increasing diversity could lead to reduction of generalization error of classifier ensembles.