Feature selection for medical dataset using rough set theory

Authors:
Yan Wang;Lizhuang Ma
Affiliations:
Department of Computer Science & Engineering, Shanghai Jiaotong University, Shanghai, P.R. China;Department of Computer Science & Engineering, Shanghai Jiaotong University, Shanghai, P.R. China
Venue:
CEA'09 Proceedings of the 3rd WSEAS international conference on Computer engineering and applications
Year:
2009

Citing 6
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Abstract

Rough set approach has been recognized to be one of the powerful tools in medical feature selection. Many feature selection methods based on rough set have been proposed, where numerous experimental results have demonstrated that these methods based on discernibility matrix are efficient. However, the high storage space and the time-consuming computation restrict its application. In this paper, we propose an efficient algorithm called as Feature Forest algorithm for generation of the reducts of a medical dataset. In the algorithm, the given dataset is transformed into a forest to form discernibility string that is the concatenation of some of features and the disjunctive normal form is computed to reduct features based on feature forest. In addition, experimental results on different datasets show that the algorithms of this paper can efficiently reduce storage cost and be computationally inexpensive.