Semigroup structure of singleton Dempster-Shafer evidence accumulation
IEEE Transactions on Information Theory
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
Maximal confidence intervals of the interval-valued belief structure and applications
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Multi-sensor data fusion based on fuzzy integral in AR system
ICAT'06 Proceedings of the 16th international conference on Advances in Artificial Reality and Tele-Existence
Evidence relationship matrix and its application to d-s evidence theory for information fusion
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Multisensor data fusion: A review of the state-of-the-art
Information Fusion
A new method to determine basic probability assignment from training data
Knowledge-Based Systems
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Dempster-Shafer evidence theory (DSET) is a popular paradigm for dealing with uncertainty and imprecision. Its corresponding evidential reasoning framework is theoretically attractive. However, there are outstanding issues that hinder its use in real-life applications. Two prominent issues in this regard are 1) the issue of basic probability assignments (masses) and 2) the issue of dependence among information sources. This paper attempts to deal with these issues by utilizing neural networks in the context of pattern classification application. First, a multilayer perceptron neural network with the mean squared error as a cost function is implemented to calculate, for each information source, posteriori probabilities for all classes. Second, an evidence structure construction scheme is developed for transferring the estimated posteriori probabilities to a set of masses along with the corresponding focal elements, from a Bayesian decision point of view. Third, a network realization of the Dempster-Shafer evidential reasoning is designed and analyzed, and it is further extended to a DSET-based neural network, referred to as DSETNN, to manipulate the evidence structures. In order to tackle the issue of dependence between sources, DSETNN is tuned for optimal performance through a supervised learning process. To demonstrate the effectiveness of the proposed approach, we apply it to three benchmark pattern classification problems. Experiments reveal that the DSETNN outperforms DSET and provide encouraging results in terms of classification accuracy and the speed of learning convergence.