Abstract
This paper proposes a new feature selection method based on rough sets to take away the unnecessary attributes for the classification process from partially uncertain decision system. The uncertainty exists only in the decision attributes (classes) and is represented by the belief function theory. The simplification of the uncertain decision table to generate more significant attributes is based on computing all possible reducts. To obtain these reducts, we propose a new definition of the concepts of discernibility matrix and function under the belief function framework. Experimentations have been done to evaluate this exhaustive solution.
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Trabelsi, S., Elouedi, Z., Lingras, P. (2013). Exhaustive Search with Belief Discernibility Matrix and Function. In: Zaïane, O.R., Zilles, S. (eds) Advances in Artificial Intelligence. Canadian AI 2013. Lecture Notes in Computer Science(), vol 7884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38457-8_14
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DOI: https://doi.org/10.1007/978-3-642-38457-8_14
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