Heuristic for attribute selection using belief discernibility matrix

Authors:
Salsabil Trabelsi;Zied Elouedi;Pawan Lingras
Affiliations:
Larodec, Institut Superieur de Gestion de Tunis, Tunisia;Larodec, Institut Superieur de Gestion de Tunis, Tunisia;Saint Mary's University Halifax, Canada
Venue:
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Year:
2012

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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.