Journal
INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volume 47, Issue 2, Pages 233-246Publisher
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ijar.2007.05.001
Keywords
classification; interval-valued information systems; knowledge discovery; knowledge reduction; rough sets
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A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects decision rules with a minimal set of features for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables. The minimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments. (c) 2007 Elsevier Inc. All rights reserved.
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