A supervised and multivariate discretization algorithm for rough sets

Authors:
Feng Jiang;Zhixi Zhao;Yan Ge
Affiliations:
College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, P.R. China;Training Department, Jining Institute of Technology, Jining, P.R. China;College of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, P.R. China
Venue:
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Year:
2010

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Abstract

Rough set theory has become an important mathematical tool to deal with imprecise, incomplete and inconsistent data. As we all know, rough set theory works better on discretized or binarized data. However, most real life data sets consist of not only discrete attributes but also continuous attributes. In this paper, we propose a supervised and multivariate discretization algorithm -- SMD for rough sets. SMD uses both class information and relations between attributes to determine the discretization scheme. To evaluate algorithm SMD, we ran the algorithm on real life data sets obtained from the UCI Machine Learning Repository. The experimental results show that our algorithm is effective. And the time complexity of our algorithm is relatively low, compared with the current multivariate discretization algorithms.