A neural network and iterative optimization hybrid for Dempster-Shafer clustering
<|reference_start|>A neural network and iterative optimization hybrid for Dempster-Shafer clustering: In this paper we extend an earlier result within Dempster-Shafer theory ["Fast Dempster-Shafer Clustering Using a Neural Network Structure," in Proc. Seventh Int. Conf. Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 98)] where a large number of pieces of evidence are clustered into subsets by a neural network structure. The clustering is done by minimizing a metaconflict function. Previously we developed a method based on iterative optimization. While the neural method had a much lower computation time than iterative optimization its average clustering performance was not as good. Here, we develop a hybrid of the two methods. We let the neural structure do the initial clustering in order to achieve a high computational performance. Its solution is fed as the initial state to the iterative optimization in order to improve the clustering performance.<|reference_end|>
@article{schubert2003a,
    title={A neural network and iterative optimization hybrid for Dempster-Shafer
  clustering},
    author={Johan Schubert},
    journal={in Proceedings of EuroFusion98 International Conference on Data
  Fusion (EF'98), M. Bedworth, J. O'Brien (Eds.), pp. 29-36, Great Malvern, UK,
  6-7 October 1998},
    year={2003},
    number={FOA-B-98-00383-505-SE},
    archivePrefix={arXiv},
    eprint={cs/0305024},
    primaryClass={cs.AI cs.NE}
}