Abstract
This paper proposes a new heuristic attribute selection method based on rough sets to remove the superfluous attributes from partially uncertain data. We handle uncertainty only in decision attributes (classes) under the belief function framework. The simplification of the uncertain decision table which is based on belief discernibility matrix generates more significant attributes with fewer computations without making significant sacrifices in classification accuracy.
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Trabelsi, S., Elouedi, Z., Lingras, P. (2012). Heuristic for Attribute Selection Using Belief Discernibility Matrix. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_17
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DOI: https://doi.org/10.1007/978-3-642-31900-6_17
Publisher Name: Springer, Berlin, Heidelberg
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