Abstract
In this paper, we deal with the problem of attribute selection from a sample of partially uncertain data. The uncertainty exists in decision attributes and is represented by the Transferable Belief Model (TBM), one interpretation of the belief function theory. To solve this problem, we propose dynamic reduct for attribute selection to extract more relevant and stable features for classification. The reduction of the uncertain decision table using this approach yields simplified and more significant belief decision rules for unseen objects.
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Trabelsi, S., Elouedi, Z., Lingras, P. (2009). Dynamic Reduct from Partially Uncertain Data Using Rough Sets. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_19
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DOI: https://doi.org/10.1007/978-3-642-10646-0_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10645-3
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