Abstract
Data uncertainty is seen as one of the main issues of several real world applications that can affect the decision of experts. Several studies have been carried out, within the data mining and the pattern recognition fields, for processing the uncertainty that is associated to the classifier outputs. One solution consists of transforming classifier outputs into evidences within the framework of belief functions. To gain the best performance, ensemble systems with belief functions have been well studied for several years now. In this paper, we aim to construct an ensemble of the Evidential Editing k-Nearest Neighbors classifier (EEk-NN), which is an extension of the standard k-NN classifier for handling data with uncertain attribute values expressed within the belief function framework, through rough set reducts.
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Trabelsi, A., Elouedi, Z., Lefevre, E. (2018). Ensemble Enhanced Evidential k-NN Classifier Through Rough Set Reducts. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_33
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