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| Decision-Theoretic Rough Set Attribute Reduction and Classification Based on Fuzzification |
| GUO Min1, JIA Xiu-Yi2, SHANG Lin1 |
1State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093
2School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094 |
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Abstract The decision-theoretic rough set (DTRS) is a kind of probabilistic rough set model with certain tolerance based on the Bayesian risk minimization principle. However, the current research on DTRS model is restricted to processing information tables with discrete data. In this paper, the decision-theoretic rough set theory is combined with fuzzy sets, and the fuzzy membership functions are employed to replace the posterior probability calculating method when calculating the expected risk losses in the DTRS model. Thus, the new decision rules are derived to effectively deal with the information system with continuous data. Experiments show that the proposed method is feasible and it has a better classification performance by adjusting the membership functions.
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Received: 26 June 2013
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2014, Vol. 27 
