Abstract
Dempster-Shafer theory has proven to be one of the most powerful tools for data fusion and reasoning under uncertainty. Despite the huge number of frameworks proposed in this area, determining the basic probability assignment remains an open issue. To address this problem, this paper proposes a novel Dempster-Shafer scheme based on Parzen-Rosenblatt windowing for multi-attribute data classification. More explicitly, training data are used to construct approximate distributions for each hypothesis, and per each data attribute, using Parzen-Rosenblatt window density estimation. Such distributions are then used at the classification stage, to generate mass functions and reach a consensus decision using the pignistic transform. To validate the proposed scheme, experiments are carried out on some pattern classification benchmarks. The results obtained show the interest of the proposed approach with respect to some recent state-of-the-art methods.
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Hamache, A. et al. (2018). Uncertainty-Aware Parzen-Rosenblatt Classifier for Multiattribute Data. In: Destercke, S., Denoeux, T., Cuzzolin, F., Martin, A. (eds) Belief Functions: Theory and Applications. BELIEF 2018. Lecture Notes in Computer Science(), vol 11069. Springer, Cham. https://doi.org/10.1007/978-3-319-99383-6_14
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