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Decision rule mining using classification consistency rate
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DOI：10.1016/j.knosys.2013.01.010.png)
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Abstract
En 中文
Decision rule mining is an important technique in many applications. In this paper, we propose a new rough set approach for rule induction based on a significance measure, called classification consistency rate. The approach implements the rule induction from the viewpoint of attribute rather than descriptor. The proposed algorithm is tested and compared with LEM2 algorithm on several real-life data sets added with different levels of inconsistent data. The results show that the proposed algorithm is effective in rule induction for inconsistent data. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Decision rules
Rule learning
Rough sets
Inconsistent decision tables
Classification consistency rate
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